Neurochaos feature transformation for Machine Learning

Sethi, Deeksha and Nagaraj, Nithin and Harikrishnan, Nellippallil Balakrishnan (2023) Neurochaos feature transformation for Machine Learning. Integration, 90. pp. 157-162.

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Abstract: With today’s increasing data complexity, efficient feature extraction has become an integral part of learning. In this spirit, we use features from a recently proposed brain-inspired learning algorithm for classification namely Neurochaos Learning (NL). This paper compares NL: chaos-based hybrid ML architectures to stand-alone ML/NL architectures. The maximum performance boost obtained is 25.97% (Statlog (Heart) dataset using NL features+Decision Tree) in the high training sample regime and 144.38% (Haberman’s Survival dataset using NL features+Random Forest) in the low training sample regime. NL offers enormous flexibility in integrating Neurochaos features with any ML classifier.
Item Type: Journal Paper
Subjects: School of Humanities > Consciousness Studies
School of Humanities > Cognitive Science
Divisions: Schools > Humanities
Date Deposited: 11 Apr 2023 04:16
Last Modified: 11 Apr 2023 04:16
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