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Learning from limited and imbalanced data is a challenging problem in theArtificial Intelligence community. Real-time scenarios demand decision-makingfrom rare events wherein the data are typically imbalanced. These situationscommonly arise in medical applications, cybersecurity, catastrophic predictionsetc. This motivates the development of learning algorithms capable of learningfrom imbalanced data. Human brain effortlessly learns from imbalanced data.Inspired by the chaotic neuronal firing in the human brain, a novel learningalgorithm namely Neurochaos Learning (NL) was recently proposed. NL iscategorized in three blocks: Feature Transformation, Neurochaos FeatureExtraction (CFX), and Classification. In this work, the efficacy of neurochaosfeature transformation and extraction for classification in imbalanced learningis studied. We propose a unique combination of neurochaos based featuretransformation and extraction with traditional ML algorithms. The exploreddatasets in this study revolve around medical diagnosis, banknote frauddetection, environmental applications and spoken-digit classification. In thisstudy, experiments are performed in both high and low training sample regime.In the former, five out of nine datasets have shown a performance boost interms of macro F1-score after using CFX features. The highest performance boostobtained is 25.97% for Statlog (Heart) dataset using CFX+Decision Tree. In thelow training sample regime (from just one to nine training samples per class),the highest performance boost of 144.38% is obtained for Haberman's Survivaldataset using CFX+Random Forest. NL offers enormous flexibility of combiningCFX with any ML classifier to boost its performance, especially for learningtasks with limited and imbalanced data.

Neurochaos Feature Transformation and Classification for Imbalanced Learning