Package: cognitivefactory-features-maximization-metric
Features Maximization MetricImplementation ofFeatures Maximization Metric, an unbiased metric aimed at estimate the quality of an unsupervised classification.Quick descriptionFeatures Maximization(FMC) is a features selection method described inLamirel, J.-C., Cuxac, P., & Hajlaoui, K. (2016). A Novel Approach to Feature Selection Based on Quality Estimation Metrics. In Advances in Knowledge Discovery and Management (pp. 121–140). Springer International Publishing. https://doi.org/10.1007/978-3-319-45763-5_7.This metric is computed by applying the following steps:Compute theFeatures F-Measuremetric (based onFeatures RecallandFeatures Predominancemetrics).(a) TheFeatures RecallFR[f][c]for a given classcand a given featurefis the ratio between
the sum of the vectors weights of the featureffor data in classcand the sum of all vectors weights of featureffor all data.
It answers the question: "Can the featurefdistinguish the classcfrom other classesc'?"(b) TheFeatures PredominanceFP[f][c]for a given classcand a given featurefis the ratio between
the sum of the vectors weights of the featureffor data in classcand the sum of all vectors weights of all featuref'for data in classc.
It answers the question: "Can the featurefbetter identify the classcthan the other featuresf'?"(c) TheFeatures F-MeasureFM[f][c]for a given classcand a given featurefis
the harmonic mean of theFeatures Recall(a) and theFeatures Predominance(c).
It answers the question: "How much information does the featurefcontain about the classc?"Compute theFeatures Selection(based onF-Measure Overall Averagecomparison).(d) TheF-Measure Overall Averageis the average ofFeatures F-Measure(c) for all classescand for all featuresf.
It answers the question: "What are the mean of information contained by features in all classes ?"(e) A featurefisSelectedif and only if it exist at least one classcfor which theFeatures F-Measure(c)FM[f][c]is bigger than theF-Measure Overall Average(d).
It answers the question: "What are the features which contain more information than the mean of information in the dataset ?"(f) A FeaturefisDeletedif and only if theFeatures F-Measure(c)FM[f][c]is always lower than theF-Measure Overall Average(d) for each classc.
It answers the question: "What are the features which do not contain more information than the mean of information in the dataset ?"Compute theFeatures ContrastandFeatures Activation(based onF-Measure Marginal Averagescomparison).(g) TheF-Measure Marginal Averagesfor a given featurefis the average ofFeatures F-Measure(c) for all classescand for the given featuref.
It answers the question: "What are the mean of information contained by the featurefin all classes ?"(h) TheFeatures ContrastFC[f][c]for a given classcand a given selected featurefis the ratio between
theFeatures F-Measure(c)FM[f][c]and theF-Measure Marginal Averages(g) for selected feature f
put to the power of anAmplification Factor.
It answers the question: "How relevant is the featurefto distinguish the classc?"(i) A selected FeaturefisActivefor a given classcif and only if theFeatures Contrast(h)FC[f][c]is bigger than1.0.
It answers the question : "For which classes a selected featurefis relevant ?"This metric is anefficient methodto:identify relevant featuresof a dataset modelization;describe associationbetween vectors features and data classes;increase contrastbetween data classes.DocumentationMain documentationInstallationFeatures Maximization Metric requiresPython3.8 or above.To install withpip:# install packagepython3-mpipinstallcognitivefactory-features-maximization-metricTo install withpipx:# install pipxpython3-mpipinstall--userpipx# install packagepipxinstall--pythonpython3cognitivefactory-features-maximization-metricDevelopmentTo work on this project or contribute to it, please read:theCopier PDMtemplate documentation ;theContributingpage for environment setup and development help ;theCode of Conductpage for contribution rules.ReferencesFeatures Maximization Metric:Lamirel, J.-C., Cuxac, P., & Hajlaoui, K. (2016). A Novel Approach to Feature Selection Based on Quality Estimation Metrics. In Advances in Knowledge Discovery and Management (pp. 121–140). Springer International Publishing. https://doi.org/10.1007/978-3-319-45763-5_7V-Measure:Rosenberg, Andrew & Hirschberg, Julia. (2007). V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. 410-420.How to citeSchild, E. (2023). cognitivefactory/features-maximization-metric. Zenodo. https://doi.org/10.5281/zenodo.7646382.