1 citations · 2 across the 7 of their papers we have counts for
7 papers
Dictionary Based Pattern Entropy for Causal Direction Discovery
Harikrishnan N B, Shubham Bhilare, Aditi Kathpalia +1
Discovering causal direction from temporal observational data is particularly challenging for symbolic sequences, where functional models and noise assumptions are often unavailabl…
A Compression Based Classification Framework Using Symbolic Dynamics of Chaotic Maps
Parth Naik, Harikrishnan N B
We propose a novel classification framework grounded in symbolic dynamics and data compression using chaotic maps. The core idea is to model each class by generating symbolic seque…
Chaotic Map based Compression Approach to Classification
Harikrishnan N B, Anuja Vats, Nithin Nagaraj +1
Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a no…
Causal Discovery and Classification Using Lempel-Ziv Complexity
Dhruthi, Nithin Nagaraj, Harikrishnan N B
Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this…
Permutation Decision Trees
Harikrishnan N B, Arham Jain, Nithin Nagaraj
Decision Tree is a well understood Machine Learning model that is based on minimizing impurities in the internal nodes. The most common impurity measures are Shannon entropy and Gi…
Neurochaos Feature Transformation and Classification for Imbalanced Learning
Deeksha Sethi, Nithin Nagaraj, Harikrishnan N B
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…
Cause-Effect Preservation and Classification using Neurochaos Learning
Harikrishnan N B, Aditi Kathpalia, Nithin Nagaraj
Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algor…