Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso
Yelleti Vivek, Sri Krishna Vadlamani, Vadlamani Ravi, P. Radha Krishna

TL;DR
This paper enhances quantum-inspired differential evolution algorithms for feature selection in high-dimensional medical data by integrating chaos theory and Lasso, achieving improved performance and scalability.
Contribution
It introduces chaos-generated variables into quantum-inspired metaheuristics and combines Lasso for feature pruning, significantly improving feature selection in medical classification tasks.
Findings
Chaos variables outperform random variables in metaheuristics
Lasso-assisted pruning improves feature subset quality
Parallelization speeds up the algorithm significantly
Abstract
Modern deep learning continues to achieve outstanding performance on an astounding variety of high-dimensional tasks. In practice, this is obtained by fitting deep neural models to all the input data with minimal feature engineering, thus sacrificing interpretability in many cases. However, in applications such as medicine, where interpretability is crucial, feature subset selection becomes an important problem. Metaheuristics such as Binary Differential Evolution are a popular approach to feature selection, and the research literature continues to introduce novel ideas, drawn from quantum computing and chaos theory, for instance, to improve them. In this paper, we demonstrate that introducing chaos-generated variables, generated from considerations of the Lyapunov time, in place of random variables in quantum-inspired metaheuristics significantly improves their performance on…
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Taxonomy
TopicsMachine Learning and ELM · Neural Networks and Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
