Dimensionality reduction using genetic algorithms
Top Cited Papers
- 1 July 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 4 (2), 164-171
- https://doi.org/10.1109/4235.850656
Abstract
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.Keywords
This publication has 26 references indexed in Scilit:
- Adaptive fuzzy rule-based classification systemsIEEE Transactions on Fuzzy Systems, 1996
- Selecting fuzzy if-then rules for classification problems using genetic algorithmsIEEE Transactions on Fuzzy Systems, 1995
- Simplifying decision treesInternational Journal of Man-Machine Studies, 1987
- Bootstrap Techniques for Error EstimationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
- Induction of decision treesMachine Learning, 1986
- The Assessment of Laboratory Tests in the Diagnosis of Acute AppendicitisAmerican Journal of Clinical Pathology, 1983
- The Jackknife, the Bootstrap and Other Resampling PlansPublished by Society for Industrial & Applied Mathematics (SIAM) ,1982
- Bootstrap Methods: Another Look at the JackknifeThe Annals of Statistics, 1979
- The protein data bank: A computer-based archival file for macromolecular structuresJournal of Molecular Biology, 1977
- On Estimation of a Probability Density Function and ModeThe Annals of Mathematical Statistics, 1962