Dimensionality reduction using genetic algorithms

• Published in: IEEE transactions on evolutionary computation Vol. 4; no. 2; pp. 164 - 171
• Main Authors: Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-07-2000; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Binding sites; Classification; Classifiers; Computer science; control theory; systems; Costs; Data mining; Data visualization; Discriminant analysis; Exact sciences and technology; Extraction; Feature extraction; Genetic algorithms; Linear discriminant analysis; Mathematical analysis; Nearest neighbor searches; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Proteins; Studies; Vectors; Vectors (mathematics); Dimensionality; Reduction; Image processing; Genetic algorithm; Selection; Linear transformation; Data visualization; Pattern classification; Classification; Pattern recognition; Pattern extraction
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/4235.850656

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.