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Efficient huge-scale feature selection with speciated genetic algorithm

This study presents a specialized genetic algorithm for effective feature selection at a large scale, focusing on the challenges of high-dimensional data. By introducing an innovative method called Speciated Genetic Algorithm for Neural Networks (SGANN), the authors improve the genetic operations of crossover and mutation while incorporating fitness sharing to enhance speciation. The proposed approach addresses the neighborhood density and enables the handling of chromosomes containing up to 25 genes for efficient feature extraction. Published in Pattern Recognition Letters, this work showcases a significant advance in machine learning methodologies.

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Efficient huge-scale feature selection with speciated genetic algorithm

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  1. Efficient huge-scale feature selection with speciated genetic algorithm Pattern Recognition Letters 27 (2006) 143–150 Jin-Hyuk Hong, Sung-Bae Cho Yonsei University, Korea Coffee Talk

  2. Standard Genetic Algorithm • - # chromosomes • fitness  P(reproduction) • Crossover • Mutation Coffee Talk

  3. Proposed method SGANN Coffee Talk

  4. Speciation by fitness sharing 0 <= similarity <= 1 mi : neighborhood density Coffee Talk

  5. Split chromosome for huge-scale feature selection The chromosome has just 25 genes (indices instead of 01) Coffee Talk

  6. 72x7219 Coffee Talk

  7. Coffee Talk

  8. Coffee Talk

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