Patent ID: 12197540

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. Note that the same portions in the description of the drawings will be denoted by the same reference numerals and signs, and the description thereof will be omitted.

Learning System

With reference toFIG.1, a learning system5according to an embodiment of the present disclosure will be described. The learning system5includes a plurality of devices1and a learning apparatus2. Both of the plurality of devices1and the learning apparatus are communicably connected to each other via various communication networks3such as the Internet, mobile communication, near field communication, or satellite communication.

Each of the devices1transmits, to the learning apparatus2, samples extracted by means of a predetermined calculation method among samples acquired from a sensor. Though the learning system5illustrated inFIG.1includes three devices1, the number of devices1is not limited. Though the embodiment of the present disclosure illustrates a case where one device1acquires samples from one sensor and transmits the samples to the learning apparatus2, the present disclosure is not limited thereto. As another example, a device1may acquire samples from a plurality of sensors, extract samples to be transmitted to a learning apparatus2from the samples acquired from the sensors, and transmit the extracted samples to the learning apparatus2.

The learning apparatus2learns samples acquired from one or more devices1to form a learning model. The learning apparatus2is a computer connected to the one or more devices1, and may be, for example, a computer provided in a cloud, or a fog node. The learning apparatus2is a so-called classifier, but the details of the classifier are not limited. In a case where the samples relate to electrocardiogram (ECG), the learning apparatus2analyzes arrhythmia (see AAMI EC57). In a case where the samples relate to electromyography (EMG), the learning apparatus2analyzes quality of skills in sport operations.

The learning apparatus2also calculates an effectiveness for each sample in learning and converts the calculated effectiveness into an effectiveness for each cluster generated by the device to feedback the effectiveness for each cluster to the device1. The device1refers to the effectiveness provided from the learning apparatus2and extracts, from among the samples acquired by the sensor, samples to be transmitted to the learning apparatus2.

In addition, the learning apparatus2calculates a ratio ε, which is a ratio used by the device1for extracting the samples, and transmits the calculated ratio ε to the device1. This ratio ε is a ratio of the number of samples selected to achieve enhancement of learning efficiency in the learning apparatus2to the number of samples transmitted to the learning apparatus2. Samples of a ratio 1-ε of the total number of samples transmitted to the learning apparatus2, are extracted such that a distribution of the samples collected by the device and a distribution of the samples transmitted to the learning apparatus2have the same tendency.

In the embodiment of the present disclosure, one cycle is defined as a cycle in which the device1acquires samples and transmits the acquired samples to the learning apparatus2, and then the learning apparatus2learns the samples and feeds back the effectiveness. The learning system5reflects learning results by the learning apparatus2in a previous cycle on sample extraction by the device1in a next cycle to properly extract samples that are effective for learning.

With reference toFIG.2, a learning method in the learning system5according to the embodiment of the present disclosure will be described. One cycle is defined as a cycle from Step S2of acquiring a plurality of samples to Step S11in which the learning apparatus2transmits an effectiveness for each cluster to the device1. The learning system5performs learning by repeating the one cycle multiple times.

First, at Step S1, the learning apparatus2transmits, to the device1, an effectiveness of each cluster and a ratio ε. The effectiveness is calculated for each cycle and cluster, and ratio ε is calculated for each cycle. The effectiveness of each cluster and the ratio ε are calculated from samples transmitted by the device1in the previous cycle. The effectiveness of each cluster and the ratio ε0calculated in the previous cycle are referenced in a current cycle at or after Step S2.

With reference to Step S2to Step S11, processing operations in the current cycle according to the embodiment of the present disclosure will be described.

At Step S2, the device1acquires a plurality of samples. In a case where a sensor output acquired by the device1is a time-series signal, the device1divides the signal into a plurality of samples. At Step S3, the device1divides the plurality of samples acquired at Step S2into a plurality of clusters.

At Step S4, the device1calculates, from the plurality of samples acquired at Step S2, three indices used to extract samples to be transmitted to the learning apparatus2. The three indices are frequency α, diversity β, and effectiveness γ. Frequency α and effectiveness γ are calculated for each cluster. Frequency α and effectiveness γ are indices for determining a ratio of the number of samples to be extracted from a cluster and transmitted to the learning apparatus2. Diversity β is calculated for each sample. Diversity β is an index of a weight for selecting samples to be transmitted from a cluster to the learning apparatus2.

At Step S5, the device1determines the number of samples for enhancing learning efficiency and the number of samples for ensuring tendency. The ratio of the number of samples for enhancing learning efficiency to the number of samples for ensuring tendency is ε:1-ε.

At Step S6, the device1extracts, in first sampling processing, samples for enhancing learning efficiency from each cluster. The number of samples extracted here is a number obtained by multiplying the number of samples to be transmitted to the learning apparatus2by ratio ε.

At Step S7, the device1extracts, in second sampling processing, samples for ensuring tendency from each cluster. The number of samples extracted here is a number obtained by multiplying the number of samples to be transmitted to the learning apparatus2by ratio 1-ε.

At Step S8, the device1transmits the samples extracted from each cluster at Steps S6and S7to the learning apparatus2.

At Step S9, the learning apparatus2performs learning using the samples acquired at Step S8. At Step S10, the learning apparatus2calculates an effectiveness γ for each cluster and further calculates a ratio ε. At Step S11, the learning apparatus2transmits the effectiveness γ of each cluster and the ratio ε calculated at Step S10to the device1.

Note that the effectiveness γ of each cluster and the ratio ε transmitted at Step S11are referenced when samples are extracted in a next cycle.

Device

With reference toFIG.3, the device1according to the embodiment of the present disclosure will be described. The device1stores feedback data11, sample data12, cluster data13, index data14, and extraction data15. The device1includes a reception unit21, an acquisition unit22, a clustering unit23, an index calculation unit24, an extraction unit25, and a transmission unit26. The functions illustrated inFIG.3are implemented by a computer executing a program for executing processing operations.

The reception unit21acquires the feedback data11from the learning apparatus2. The feedback data11includes an effectiveness of each cluster and a ratio ε. The effectiveness of each cluster and the ratio ε received by the reception unit21from the learning apparatus2are calculated from samples in the previous cycle.

The acquisition unit22acquires a plurality of samples from a sensor6, for example. The samples are single-cycle signals detected by the sensor6. When the sensor6continuously detects a motion of a plurality of cycles, the acquisition unit22converts a continuous signal into a set of single-cycle signals.

When the sensor6inputs a time-series signal as illustrated inFIG.4(a)to the device1, the acquisition unit22sets time windows for the input time-series signal, performs noise removal and normalization of data for each window, and acquires samples which are signals of a single-cycle motion as illustrated inFIG.4(b).

Here, a size of the time window depends on the location where the sensor is installed. The time window is, for example, 4 seconds, 10 seconds, 1 minute, or the like in which a certain degree of consistency of movement in an exercise is observed. In a case where the sensor measures cardiac potential, due to QRS complex (R wave) detection by threshold processing, a length of a time window will be a duration of one heartbeat with the R wave as a reference. In a case where the sensor measures myoelectric potential, due to onset detection by threshold processing or by dynamic threshold processing, a length of a time window will be a duration of one cycle in a periodic motion with an onset start time as a reference. In order to make lengths of the samples constant, a length of a sample for one heartbeat may be extended or reduced for adjustment.

The acquisition unit22stores the signal of each sample as the sample data12. Here, the signal of each sample may be a waveform itself or may be a feature calculated from the waveform by predetermined processing.

The clustering unit23divides a plurality of samples identified in the sample data12into a plurality of clusters. Here, the clustering unit23divides the plurality of samples into a predetermined number of clusters. The clustering unit23generates the cluster data13that identifies samples belonging to each cluster. In the cluster data13, for example, an identifier of a cluster is associated with an identifier of a sample belonging to the cluster.

The clustering unit23classifies the plurality of samples into the plurality of clusters using a common clustering method such as K-shape or shapelet discovery.

With reference toFIG.5, clustering processing performed by the clustering unit23will be described.

First, at Step S101, the clustering unit23determines an initial value of centroid for a prescribed number of clusters. In the first cycle where device1first performs clustering, a centroid is determined by a random number. In the second and subsequent cycles, a centroid in a previous cycle is set as the initial value of a centroid in a current cycle.

At Step S102, the clustering unit23classifies each sample into a cluster having a closest centroid. Here, it is assumed that each sample belongs to the cluster having the closest centroid.

At Step S103, whether the clustering has converged is determined. For example, if a sum of the distances between samples and a centroid of the cluster to which the samples belong converges, the clustering is determined to converge. The distances are calculated by means of shape-based distance (SBD), dynamic time warping (DTW), or the like, for example. Alternatively, if a centroid calculated from each sample belonging to a cluster coincides with the centroid serving as a criterion of classification at Step S102, the clustering is determined to converge.

If the clustering is determined not to converge, then a new centroid is calculated at Step S104and the processing of Step S102is repeated. The clustering unit23changes, according to the classification at Step S102, coordinates of the centroid of the cluster to the location where the sum of the distances from the locations of the samples belonging to the cluster is minimized. At Step S102, the clustering unit23calculates a centroid for each cluster.

When determining that the clustering has converged, the clustering unit23generates the cluster data13at Step S105and ends the processing.

The index calculation unit24calculates indices to be referenced when the extraction unit25extracts samples to be transmitted to the learning apparatus2from each cluster. The indices are the number of samples for enhancing learning efficiency and the number of samples for ensuring tendency, as well as frequency α, diversity β, and effectiveness γ, which are indices when the extraction unit25extracts samples for enhancing learning efficiency from each cluster. The index calculation unit24generates the index data14including the calculated indices.

The index calculation unit24uses ratio ε to calculate the number of samples for enhancing learning efficiency and the number of samples for ensuring tendency. The number of samples for enhancing learning efficiency is the total number of samples to be transmitted from the device1to the learning apparatus2multiplied by ε. The number of samples for ensuring tendency is the total number of samples to be transmitted from the device1to the learning apparatus2multiplied by (1-ε).

Note that in a case where ratio ε is not given from the learning apparatus2, such as in the first cycle, the index calculation unit24uses an any ratio.

The index calculation unit24calculates frequency α and effectiveness γ for each cluster in order to determine the number to be extracted from each cluster.

Frequency α is a ratio of the number of samples belonging to a cluster to the total number of samples. For each cluster, the index calculation unit24counts the number of samples belonging to the cluster and divides the number of samples belonging to each cluster by the total number of samples to calculate frequency α for each cluster. As the number of samples belonging to a cluster increases, the frequency of the cluster becomes higher.

Effectiveness γ is an effectiveness in learning the samples in each cluster by the learning apparatus2. Effectiveness γ is calculated from the effectiveness of each cluster in the previous cycle acquired by the reception unit21.

The index calculation unit24may associate each cluster in a previous cycle with each cluster identified in a current cycle based on a centroid of each cluster. The index calculation unit24sets an effectiveness of a cluster in the previous cycle corresponding to a cluster identified in the current cycle as effectiveness γ of the cluster in the current cycle corresponding to the cluster in the previous cycle. In that case, samples are extracted from each of the plurality of clusters in the current cycle according to the effectiveness of each cluster in the previous cycle. For example, among centroids of clusters in the previous cycle, a cluster in the previous cycle having a centroid closest to a centroid of a cluster in the current cycle is identified as a cluster corresponding to the cluster in the current cycle. This calculation method is effective when similar clusters are formed in each cycle. Note that in a case where effectiveness of each cluster is not given from the learning apparatus2, such as in the first cycle, an initial value of γ may be 0 or the like.

An effectiveness of each cluster to be transmitted from the learning apparatus2is an effectiveness of the cluster in a previous cycle. Therefore, effectiveness γ for each cluster in a current cycle may be calculated by correcting the effectiveness in the previous cycle to the effectiveness in the current cycle.

FIG.6illustrates a portion of samples of a cluster in a previous cycle and a cluster in a current cycle corresponding to the cluster in the previous cycle, for example. InFIG.6, open squares are samples of a cluster in a previous cycle and solid squares are samples of a cluster in a current cycle. A centroid of the cluster in the current cycle is indicated by C1and a centroid of the cluster in the previous cycle is indicated by C2. Thus, even in two corresponding clusters in two cycles, the positions of the centroids are different. The index calculation unit24then corrects the effectiveness of the cluster in the previous cycle according to the distance between the centroid of the cluster in the previous cycle and the centroid of the cluster in the current cycle corresponding to the cluster in the previous cycle and calculates the effectiveness of the cluster in the current cycle corresponding to the cluster in the previous cycle. For example, the effectiveness of the cluster in the current cycle corresponding to the cluster in the previous cycle is calculated such that as the distance between the centroid of the cluster in the previous cycle and the centroid of the cluster in the current cycle corresponding to the cluster in the previous cycle increases, the effectiveness of the cluster in the current cycle becomes lower than the effectiveness of the cluster in the previous cycle.

Alternatively, an effectiveness of a cluster in a current cycle may be calculated according to distances between a centroid of the cluster in the current cycle to be processed and centroids of a plurality of clusters in a previous cycle.FIG.7illustrates centroids P1, P2, and P3of clusters in a previous cycle and a centroid C1of a cluster in a current cycle, for example. The effectiveness of the cluster in the current cycle is calculated such that the effectiveness of a cluster in the previous cycle having a centroid that is close in distance to the centroid C1is strongly reflected and the effectiveness of a cluster in the previous cycle having a centroid that is distant from the centroid C1is weakly reflected. In the example illustrated inFIG.7, the effectiveness of the cluster having the centroid C1is calculated from the effectiveness of each of the clusters having centroids P1, P2and P3so as to be strongly affected by the effectiveness of the cluster in the previous cycle having the centroid P1that is close in distance to the centroid C1and is weakly affected by the effectiveness of the cluster in the previous cycle having the centroid P2that is distant from the centroid C1.

The index calculation unit24may, for example, calculate an effectiveness of a cluster in a current cycle on the basis of a weight negatively correlated with a distance between a position of a centroid of the cluster in the current cycle and a position of a centroid of the cluster in the previous cycle. Specifically, the index calculation unit24may repeat, for each cluster in the previous cycle, processing of multiplying the effectiveness of the cluster in the previous cycle by an inverse of the distance between the position of the centroid of the cluster in the current cycle and the position of the centroid of the cluster in the previous cycle and may sum up values obtained in each processing operation to obtain the effectiveness of the cluster in the current cycle. In this case, for calculating the effectiveness of the cluster in the current cycle, the index calculation unit24may calculate the effectiveness of the cluster in the current cycle from the effectiveness of each cluster in the previous cycle or may calculate the effectiveness of the cluster in the current cycle from, among each cluster in the previous cycle, the effectiveness of each cluster within a predetermined range from the cluster in the current cycle to be calculated. As a result, it is possible to appropriately correct the effectiveness of the cluster in the current cycle in consideration of a difference in the centroid between the cluster in the previous cycle and the cluster in the current cycle.

The index calculation unit24calculates diversity β for each sample in order to determine samples to be extracted from each cluster. Diversity β is calculated to be higher as a distance between a sample and a centroid of a cluster to which the sample belongs increases. The distance used here is calculated by means of a shape-based distance (SBD). The index calculation unit24calculates, for example, an inverse of a distance between a sample and a centroid of a cluster to which the sample belongs as diversity β.

With reference toFIG.8, index calculation processing performed by the index calculation unit24will be described.

At Step S201, the index calculation unit24calculates the number of samples for enhancing learning efficiency and the number of samples for ensuring tendency.

Then, the processing operations of Steps S202to S204are repeated for each cluster. At Step S202, the index calculation unit24calculates frequency α of a cluster to be processed from a ratio of the number of samples belonging to the cluster to be processed to the number of previous samples. At Step S203, the index calculation unit24calculates effectiveness γ of the cluster to be processed from an effectiveness of a cluster in the previous cycle having a centroid closest to a centroid of the cluster to be processed. Note that the calculation method of effectiveness γ illustrated inFIG.8is an example, and various calculation methods are conceivable, such as correcting by a distance from a centroid of a cluster in the previous cycle or calculating from pieces of effectiveness of a plurality of clusters in the previous cycle.

The index calculation unit24performs the processing of Step S204for each sample belonging to the cluster to be processed. At Step S204, the index calculation unit24calculates diversity β from a distance between the sample to be processed and a centroid of the cluster to be processed. The calculation is performed such that a value of diversity β is higher as the distance from the centroid increases.

When the processing of Step S204for each sample belonging to the cluster to be processed is completed, the processing operations of Steps S202to S204are repeated for other clusters. After the processing operations of Steps S202to S204have been repeated for each cluster, the index calculation unit24ends the processing.

Based on an index indicated by index data14, the extraction unit25extracts samples from each cluster with reference to the sample data12and the cluster data13. The extraction unit25performs first sampling processing to extract samples for enhancing learning efficiency and second sampling processing to extract samples for ensuring tendency. The extraction unit25generates extraction data15that identifies samples extracted in the first sampling processing and in the second sampling processing. The extraction data15identifies the extracted samples and also identifies the identifier of the cluster to which the samples belong.

The extraction unit25calculates importance for each cluster in the first sampling processing to extract more samples from clusters having higher importance and extract less samples from clusters having lower importance. The importance is positively correlated with frequency α or effectiveness γ. The importance may be frequency α or effectiveness γ, or may be a composite index of frequency α and effectiveness γ. The importance is calculated, for example, by adding or multiplying the frequency α and effectiveness γ, multiplying a square root of the frequency α by the effectiveness γ, or the like. In the first sampling processing, the number of samples obtained by multiplying the total number of samples to be transmitted to the learning apparatus2by ε is extracted.

In a case where the importance is effectiveness γ, the extraction unit25extracts samples from each of the plurality of clusters according to the effectiveness of each cluster received from the learning apparatus2. The extraction unit25extracts samples from each cluster with reference to effectiveness γ of each cluster of the index data14. As described with regard to the index calculation unit24, effectiveness γ used here may be the effectiveness of each cluster received from the learning apparatus2or may be an effectiveness calculated by correcting the effectiveness of each cluster. More samples are extracted from clusters with higher effectiveness γ, and fewer samples are extracted from clusters with lower effectiveness γ. The extraction unit25specifies the number of samples to be extracted from each cluster according to a ratio of effectiveness γ.

The extraction unit25extracts the specified number of samples from each cluster. When samples are extracted from a certain cluster, each sample has diversity β negatively correlated with a distance to a centroid of a cluster to which the sample belongs as a weight. In the extraction unit25, samples which are more distanced from the centroid of the cluster, specifically samples having higher diversity β, have a higher probability of being extracted from the cluster, and samples which are closer to the centroid of the cluster, specifically samples having lower diversity β, have a lower probability of being extracted from the cluster. Thus, by extracting the samples using diversity β negatively correlated with the distance to the centroid of the cluster as a weight, the diversity of samples extracted from the cluster is ensured.

With reference toFIG.9, the first sampling processing performed by the extraction unit25will be described. Here, a case where the importance of cluster is calculated from frequency α and effectiveness γ will be described.

First, at Step S301, the extraction unit25calculates the importance for each cluster. This importance is positively correlated with each of the frequency α and the effectiveness γ. At Step S302, the extraction unit25determines the number of samples to be extracted from each cluster according to the ratio of the importance of each cluster calculated at Step S301.

The processing of Step S303is repeated for each cluster. The extraction unit25extracts, for a cluster to be processed, samples to be transmitted among samples belonging to the cluster to be processed using diversity β of each sample as a weight. The number of samples to be extracted here is the number of samples calculated at Step S302for the cluster to be processed. When the processing of Step S303for each cluster is completed, the first sampling processing is ended.

In the first sampling processing, since the samples are extracted for the purpose of enhancing learning efficiency, the tendency of the samples to be extracted in the first sampling processing may differ from the tendency of the samples acquired by the acquisition unit22. Thus, in the second sampling processing, the extraction unit25extracts new samples from each cluster such that a distribution of samples to be transmitted to the learning apparatus2coincides with a tendency of the samples acquired by the acquisition unit22. The distance between the distributions is calculated, for example, by means of the earth mover's distance. In the second sampling processing, the number of samples obtained by multiplying the total number of samples to be transmitted to the learning apparatus2by (1-ε) is extracted. In the second sampling processing, the extraction unit25selects, for example by a greedy algorithm, the number of samples obtained by multiplying the total number of samples to be transmitted to the learning apparatus2by (1-ε) one by one.

With reference toFIG.10, the second sampling processing performed by the extraction unit25will be described.

At Step S401, the extraction unit25extracts samples to be transmitted such that a distance between a distribution of samples extracted in the first sampling processing and a distribution of samples acquired by the acquisition unit22is minimized. The extraction unit25ends the processing after the number of samples obtained by multiplying the total number of samples to be transmitted to the learning apparatus2by (1-ε) is extracted.

The transmission unit26transmits the extracted samples to the learning apparatus2. The transmission unit26transmits data of each sample identified by the extraction data15to the learning apparatus2.

Learning Apparatus

With reference toFIG.11, the learning apparatus2according to the embodiment of the present disclosure will be described. The learning apparatus2stores extraction data51, result data52, and feedback data53. The learning apparatus2includes a reception unit61, a learning unit62, a feedback calculation unit63, and a transmission unit64. The functions illustrated inFIG.3are implemented by a computer executing a program for executing processing operations.

The reception unit61receives data of samples extracted from each cluster from a device1and stores the received data in the extraction data51. Here, the reception unit61receives data of samples from each of a plurality of devices1and stores the data in the extraction data51. The extraction data51identifies each sample transmitted from each device1.

Any one of a plurality of types of labels is assigned to the samples extracted by the device1by means of any method. For example, when the learning apparatus2generates a learning model that classifies samples into three types, a label of any one of the three types is assigned to each sample identified by the extraction data51. In a case where the samples are heartbeat data, a doctor who is an operator may assign a label indicating whether each sample is irregular heartbeat. Alternatively, the samples extracted by the device1may be labeled by a software using a predetermined algorithm.

The learning unit62learns samples extracted by the device1. The learning unit62generates a learning model by learning, as an input, the extraction data51to which the label has been assigned as correct answer data. A discriminator implemented by the learning unit62is, for example, a convolutional neural network (CNN).

The learning unit62further uses the learning model generated by learning the samples extracted by the device1to calculate a probability of the sample extracted by the device1being correspondent to each of the plurality of types of labels. The learning unit62calculates a ratio of correspondence to each label using the generated learning model for each sample identified by the extraction data51. For example, in a case where there are correct answer labels A, B, and C, the learning unit62calculates, for each sample identified by the extraction data51, a probability of being labeled as A, a probability of being labeled as B, and a probability of being labeled as C. The learning unit62calculates, for example, “a probability of being A: 40%, a probability of being B: 35%, a probability of being C: 25%” for a certain sample or calculates “a probability of being A: 90%, a probability of being C: 10%” for another sample. The learning unit62stores the probability of each label for each sample in the result data52.

With reference toFIG.12, learning processing performed by the learning unit62will be described.

At Step S501, the learning unit62generates a learning model from each sample to which one correct answer label has been assigned by the operator.

For each sample identified by the extraction data51, the processing of Step S502is repeated. At Step S502, the learning unit62determines, for the samples to be processed, the probability of being correspondent to each label. When the processing of Step S502for each sample identified by the extraction data51is completed, the learning unit62ends the processing.

With reference to the result data52, the feedback calculation unit63calculates an effectiveness in learning the samples belonging to the cluster for each cluster, and a ratio ε. The feedback calculation unit63generates feedback data53to be transmitted to the device1. The feedback data53is generated for each device1that is a destination of transmission and includes the effectiveness of each cluster and the ratio ε.

The feedback calculation unit63calculates an effectiveness for each sample and calculates an average of the effectiveness of the samples belonging to each cluster as the effectiveness of that cluster. Methods for calculating the effectiveness of each sample includes a calculation method that increases an effectiveness of a cluster having larger number of samples to which special labels are assigned, and a calculation method that increases an effectiveness of a cluster having larger number of samples for which it is difficult to identify labels by learning.

First, the calculation method that increases an effectiveness of a cluster having larger number of samples to which special labels are assigned will be described. The feedback calculation unit63calculates an effectiveness of a cluster by counting, for each type of label assigned by an operator, the number of samples to which the label is assigned and calculating, for each cluster, the effectiveness of the cluster to be larger for fewer number of samples having the label assigned to the samples belonging to the cluster. For example, the feedback calculation unit63calculates, for each sample belonging to the cluster, an index that increases for fewer number of samples having the label assigned to the sample and calculates an average of the indices of the samples belonging to the cluster as the effectiveness of the cluster.

Next, the calculation method that increases an effectiveness of a cluster having larger number of samples for which it is difficult to identify labels by learning will be described. The feedback calculation unit63calculates, for each cluster, an effectiveness of the cluster to be larger for less degree of variation in the probability of being correspondent to each label for the samples belonging to the cluster. For example, the feedback calculation unit63calculates, for each sample belonging to the cluster, an index that increases for less degree of variation in the probability of being correspondent to each label and calculates an average of the indices of the samples belonging to the cluster as the effectiveness of the cluster. The degree of variation may be, for the samples to be processed, a degree of variation in the probability of each label type, or a degree of variation in the probability between the label types assigned by the operator and a predetermined number of label types having higher probability among the label types other than the label types assigned by the operator, or may be obtained by other calculation methods.

Assume that sample X has “a probability of being A: 40%, a probability of being B: 35%, a probability of being C: 25%”, and sample Y has “a probability of being A: 90%, a probability of being C: 10%”. Since data around the sample X is a sample at a boundary portion that is difficult for the learning model to identify, the effect of labeling is large for such a sample. Therefore, the feedback calculation unit63calculates, for each sample, an index based on a difference between the ratio determined by the learning model for the label to which the operator has assigned the correct answer and the ratio of the label having the highest probability determined by the learning model among the labels other than the label to which the operator has assigned the correct answer. For example, an index of 1−0.4+0.35=0.95 is calculated for sample X, and an index of 1−0.9+0.1=0.2 is calculated for sample Y. The feedback calculation unit63calculates an effectiveness for each sample so as to be proportional to such an index, and the device1extracts a larger number of samples of clusters having higher effectiveness, and then the number of samples of a boundary portion that are difficult for the learning model to identify can be increased. As a result, the learning system5can generate a learning model that can make determination more appropriately.

The feedback calculation unit63may calculate the effectiveness of each cluster from the effectiveness calculated by means of each of the aforementioned two calculation methods. The effectiveness calculated by means of the calculation method that increases an effectiveness of a cluster having a larger number of samples to which special labels are assigned is set as a first parameter. The effectiveness calculated by means of the calculation method that increases an effectiveness of a cluster having a larger number of samples for which it is difficult to identify labels by learning is set as a second parameter. The feedback calculation unit63calculates a parameter positively correlated with each of the first parameter and the second parameter as the effectiveness. For example, the feedback calculation unit63calculates an average of the first parameter and the second parameter of a cluster as the effectiveness of the cluster.

The feedback calculation unit63further calculates ratio ε. Ratio ε is an average effectiveness calculated for samples extracted by the device1. Ratio ε may also be corrected to have the same balance as the average effectiveness calculated for devices1, such as twice the average effectiveness, rather than the average effectiveness. Since ratio ε is calculated to be higher for a device from which a larger number of effective samples have been extracted, the device1can contribute to enhancement in learning efficiency by increasing the ratio of samples for increasing efficiency.

Ratio ε may be adjusted depending on the characteristics of the device1. For example, ratio ε is adjusted to be larger for a newly installed device1. Since samples of the newly installed device1have not yet been sufficiently learned by the learning unit62, demand of sampling for enhancing learning efficiency is high. Meanwhile, ratio ε is reduced for a device1for which a considerable time has elapsed since installation. Since maturity of learning by the learning unit62is higher for samples of the device1for which a considerable time has elapsed since installation, demand of sampling for enhancing learning efficiency is reduced, and thus ratio ε is adjusted to be smaller.

With reference toFIG.13, feedback processing performed by the feedback calculation unit63will be described. Calculation of effectiveness from parameters calculated by each of the calculation method that increases an effectiveness of a cluster having a larger number of samples to which special labels are assigned and the calculation method that increases an effectiveness of a cluster having a larger number of samples for which it is difficult to identify labels by learning will be described here.

First, at Step S601, the feedback calculation unit63counts, for each type of label assigned by the operator, the number of samples to which the label is assigned. Next, for each sample transmitted by the device1, the processing operations of Steps S602to S604are repeated.

At Step S602, the feedback calculation unit63calculates, for the samples to be processed, a first parameter negatively correlated with the number of samples to which the operator has assigned the same label as the label assigned to the samples to be processed by the operator. At Step S603, the feedback calculation unit63calculates a second parameter negatively correlated with the degree of variation in the probability of each label identified by learning. At Step S604, the feedback calculation unit63calculates an effectiveness positively correlated with the first parameter calculated at Step S602and positively correlated with the second parameter calculated at Step S603.

When the processing operations of Steps S602to S604for each sample are completed, processing of Step S605is performed for each cluster. Each cluster is each cluster formed by the device1.

At Step S605, the feedback calculation unit63calculates an average of the effectiveness of each sample belonging to the cluster to be processed as an effectiveness of the cluster to be processed. When the processing of Step S605for each cluster is completed, the processing proceeds to Step S606.

At Step S606, the feedback calculation unit63calculates ratio ε of the device1to which the feedback data53is transmitted. The feedback calculation unit63calculates ratio ε from the average effectiveness of each sample calculated at Step S604. The feedback calculation unit63may adjust the calculated ratio ε based on, for example, time having elapsed from installation of the device1.

The transmission unit64transmits the feedback data53to the device1. The feedback data53includes the effectiveness of each cluster and the ratio ε. The feedback data53transmitted by the transmission unit64is referenced when samples are extracted by the device1in a next cycle.

According to the embodiment of the present disclosure, the learning apparatus2gives an effectiveness γ of each cluster to the device1such that more samples that play efficient roles in learning by the learning apparatus2are selected, and the device1extracts the number of samples proportional to the effectiveness γ from each cluster. This results in lower effectiveness of a cluster having samples to which larger number of labels are assigned by the operator and higher effectiveness of a cluster having samples to which smaller number of labels are assigned by the operator. The learning apparatus2can collect and learn more samples to which a smaller number of labels are assigned.

When the samples are identified using the learning model generated by the learning apparatus2, the effectiveness is higher for a cluster to which samples having smaller variation of the probability for labels and being difficult to identify belongs. The learning apparatus2can collect and learn more samples located at boundary portions that are difficult to identify.

When samples are extracted from each cluster by the device1, samples farther from a centroid of the cluster, specifically, samples having higher diversity are more easily extracted. In addition, since typically a large number of samples tend to gather in the vicinity of the centroid of the cluster, samples can be extracted from the entire cluster.

In addition, in the embodiment of the present disclosure, the learning apparatus2not only collects samples that are effective for learning, but also acquires a group of samples having a distribution similar to that of samples acquired by the device1. In a case where the learning apparatus collects only samples that are effective for learning, the learning apparatus2can achieve both collecting samples that are effective for learning and collecting samples that form a distribution similar to that of samples collected by the device1, even when a deviation occurs in the distribution of the samples transmitted to the learning apparatus.

In addition, the learning apparatus2also provides, to the device1, appropriate feedback for extracting samples. Accordingly, in the learning system5, the device1can extract samples and the learning apparatus2can learn the extracted samples.

As each of the device1and the learning apparatus2of the present embodiment described above, a general-purpose computer system is used. The general-purpose computer system includes a central processing unit (CPU) (a processor)901, a memory902, a storage903(a hard disk drive (HDD) or a solid state drive (SSD)), a communication device904, an input device905, and an output device906. In this computer system, the CPU901executes a predetermined program loaded in the memory902to implement each function of the device1and to implement each function of the learning apparatus2.

Note that each of the device1and the learning apparatus2may be implemented on one computer or may be implemented on a plurality of computers. Still alternatively, each of the device1and the learning apparatus2may be a virtual machine implemented on a computer.

Each program for the device1and the learning apparatus2may be stored in a computer-readable recording medium such as a HDD, a SSD, a universal serial bus (USB) memory, a compact disc (CD), or a digital versatile disc (DVD) or may be distributed via a network.

The present disclosure is not limited to the above embodiment, and various modification may be made within the scope of its gist.

REFERENCE SIGNS LIST

1Device2Learning apparatus3Communication network5Learning system6Sensor11,53Feedback data12Sample data13Cluster data14Index data15,51Extraction data21,61Reception unit22Acquisition unit23Clustering unit24Index calculation unit25Extraction unit26,64Transmission unit52Result data62Learning unit63Feedback calculation unit901CPU902Memory903Storage904Communication device905Input device906Output device