Neurochaos Feature Transformation and Classification for Imbalanced Learning

• URL: http://arxiv.org/abs/2205.06742v2
• Date: Mon, 16 May 2022 15:13:54 GMT
• Title: Neurochaos Feature Transformation and Classification for Imbalanced Learning
• Authors: Deeksha Sethi and Nithin Nagaraj and Harikrishnan N B
• Abstract summary: Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Inspired by the chaotic neuronal firing in the human brain, a novel learning algorithm namely Neurochaos Learning (NL) was recently proposed. We propose a unique combination of neurochaos based feature transformation and extraction with traditional ML algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Real-time scenarios demand decision-making from rare events wherein the data are typically imbalanced. These situations commonly arise in medical applications, cybersecurity, catastrophic predictions etc. This motivates the development of learning algorithms capable of learning from imbalanced data. Human brain effortlessly learns from imbalanced data. Inspired by the chaotic neuronal firing in the human brain, a novel learning algorithm namely Neurochaos Learning (NL) was recently proposed. NL is categorized in three blocks: Feature Transformation, Neurochaos Feature Extraction (CFX), and Classification. In this work, the efficacy of neurochaos feature transformation and extraction for classification in imbalanced learning is studied. We propose a unique combination of neurochaos based feature transformation and extraction with traditional ML algorithms. The explored datasets in this study revolve around medical diagnosis, banknote fraud detection, environmental applications and spoken-digit classification. In this study, experiments are performed in both high and low training sample regime. In the former, five out of nine datasets have shown a performance boost in terms of macro F1-score after using CFX features. The highest performance boost obtained is 25.97% for Statlog (Heart) dataset using CFX+Decision Tree. In the low training sample regime (from just one to nine training samples per class), the highest performance boost of 144.38% is obtained for Haberman's Survival dataset using CFX+Random Forest. NL offers enormous flexibility of combining CFX with any ML classifier to boost its performance, especially for learning tasks with limited and imbalanced data.

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