An active learning system comprising: a storage section for storing a set of data for each of which its label value is unknown, each of said set of data being referred to hereinafter as unknown data, and a set of data for each of which its label value is known and is other than a specific value, each of said set of data being referred to hereinafter as known data; a first data selection section that calculates a degree of similarity of each of the set of unknown data with respect to the set of known data, to select unknown data having a lowest calculated degree of similarity among the set of unknown data; an output section that outputs the selected unknown data; an input section through which a label value of the selected unknown data is input by a user; and a data update section that adds, to the set of known data, the selected unknown data, and removes the originally unknown data from the set of unknown data, thereby updating the set of unknown data; wherein the first data selection section is adapted to repeat the selection with respect to the updated set of unknown data, the active learning system further comprises: a second data selection section that learns a rule based on a set of data for each of which its label value is known and is said specific value when said set of data, being stored in the storage section, is collected, as a result of the repetition of the selection by the first data selection section, in number needed for the rule learning, and applies the learned rule to the updated set of unknown data, to predict a label value of each unknown data and select unknown data based on the predicted result, wherein the output section outputs the selected unknown data, when a label value of the selected unknown data is input through the input section by the user, the data update section is adapted to add, to the set of known data, the selected unknown data, and remove the originally stored unknown data from the set of unknown data, thereby updating the set of unknown data, and the second data selection section is adapted to repeat the learning with respect to the updated set of unknown data until a termination condition is satisfied a storage section for storing a set of data for each of which its label value is unknown, each of said set of data being referred to hereinafter as unknown data, and a set of data for each of which its label value is known and is other than a specific value, each of said set of data being referred to hereinafter as known data; a first data selection section that calculates a degree of similarity of each of the set of unknown data with respect to the set of known data, to select unknown data having a lowest calculated degree of similarity among the set of unknown data; an output section that outputs the selected unknown data; an input section through which a label value of the selected unknown data is input by a user; and a data update section that adds, to the set of known data, the selected unknown data, and removes the originally unknown data from the set of unknown data, thereby updating the set of unknown data; wherein the first data selection section is adapted to repeat the selection with respect to the updated set of unknown data, the active learning system further comprises: a second data selection section that learns a rule based on a set of data for each of which its label value is known and is said specific value when said set of data, being stored in the storage section, is collected, as a result of the repetition of the selection by the first data selection section, in number needed for the rule learning, and applies the learned rule to the updated set of unknown data, to predict a label value of each unknown data and select unknown data based on the predicted result, wherein the output section outputs the selected unknown data, when a label value of the selected unknown data is input through the input section by the user, the data update section is adapted to add, to the set of known data, the selected unknown data, and remove the originally stored unknown data from the set of unknown data, thereby updating the set of unknown data, and the second data selection section is adapted to repeat the learning with respect to the updated set of unknown data until a termination condition is satisfied a second data selection section that learns a rule based on a set of data for each of which its label value is known and is said specific value when said set of data, being stored in the storage section, is collected, as a result of the repetition of the selection by the first data selection section, in number needed for the rule learning, and applies the learned rule to the updated set of unknown data, to predict a label value of each unknown data and select unknown data based on the predicted result, wherein the output section outputs the selected unknown data, when a label value of the selected unknown data is input through the input section by the user, the data update section is adapted to add, to the set of known data, the selected unknown data, and remove the originally stored unknown data from the set of unknown data, thereby updating the set of unknown data, and the second data selection section is adapted to repeat the learning with respect to the updated set of unknown data until a termination condition is satisfied