A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Discriminant analysis in correlation similarity measure space
Proceedings of the 24th international conference on Machine learning
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
The hybrid credit scoring model based on KNN classifier
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A Hybrid Genetic Algorithm based Fuzzy Approach for Abnormal Retinal Image Classification
International Journal of Cognitive Informatics and Natural Intelligence
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Genetic algorithms are powerful tools for k-nearest neighbors classification. Traditional knn classifiers employ Euclidian distance to assess neighbor similarity, though other measures may also be used. GAs can search for optimal linear weights of features to improve knn performance using both Euclidian distance and cosine similarity. GAs also optimize additive feature offsets in search of an optimal point of reference for assessing angular similarity using the cosine measure. This poster explores weight and offset optimization for knn with varying similarity measures, including Euclidian distance (weights only), cosine similarity, and Pearson correlation. The use of offset optimization here represents a novel technique for enhancing Pearson/knn classification performance. Experiments compare optimized and non-optimized classifiers using public domain datasets. While unoptimized Euclidian knn often outperforms its cosine and Pearson counterparts, optimized Pearson and cosine knn classifiers show equal or improved accuracy compared to weight-optimized Euclidian knn.