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 has a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current 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 increasing classification efficiency, it does not necessarily reduce the number of features that must 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, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. A masking vector is also employed to perform simultaneous selection of a subset of the features. 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 identification of favorable water binding sites on protein surfaces, an important problem in biochemistry and drug design.
|
4828
|
Genetic Algorithms
– Goldberg
- 1989
|
|
2961
|
Pattern Classification and Scene Analysis
– Duba, Hart
- 1973
|
|
2489
|
Induction of Decision Trees
– Quinlan
- 1986
|
|
2138
|
UCI Repository of Machine Learning Databases
– Blake, Merz
- 1998
|
|
533
|
Nearest neighbor pattern classification
– Cover, Hart
- 1967
|
|
496
|
The use of multiple measurements in taxonomic problems
– Fisher
- 1936
|
|
279
|
On the estimation of a probability density function and the mode
– PARZEN
- 1962
|
|
254
|
The Jackknife, the Bootstrap, and Other Resampling Plans
– EFRON
- 1982
|
|
250
|
Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7:1–26
– Efron
- 1979
|
|
235
|
The Protein Data Bank: a computer-based archival file for macromolecular structures
– Bernstein, Koetzle, et al.
- 1977
|
|
178
|
Feature selection: Evaluation, application, and small sample performance
– Jain, Zongker
- 1997
|
|
175
|
Floating search methods in feature selection
– Pudil, Novovicova, et al.
- 1994
|
|
120
|
An empirical comparison of pattern recognition, neural nets, and machine learning classification methods
– Weiss, Kapouleas
- 1989
|
|
96
|
Artificial neural networks for feature extraction and multivariate data projection
– Mao, Jain
|
|
82
|
A note on genetic algorithms for largescale feature selection
– Siedlecki, Sklansky
- 1989
|
|
62
|
Dimensionality and sample size considerations in pattern recognition practice
– Jain, Chandrasekaran
|
|
61
|
Evolutionary Computation: The Fossil Record
– Fogel, editor
- 1998
|
|
52
|
Further Research on Feature Selection and Classification using Genetic Algorithms
– Punch, Goodman, et al.
- 1993
|
|
49
|
Inductive knowledge acquisition: A case study
– Quinlan, Compton, et al.
- 1987
|
|
47
|
Simulation of genetic systems by automatic digital computers
– Fraser
- 1957
|
|
33
|
Tanaka , “Selecting Fuzzy if-then Rules for Classification Problems Using Genetic Algorithms
– Ishibuchi, Nozaki, et al.
- 1995
|
|
32
|
Hybridizing the Genetic Algorithm and the K Nearest Neighbors Classification Algorithm
– Kelly, Davis
- 1991
|
|
28
|
Comparative study of techniques for large-scale feature selection
– Ferri, Pudil, et al.
- 1994
|
|
24
|
Bootstrap techniques for error estimation
– Jain, Dubes, et al.
- 1987
|
|
20
|
Global properties of evolution processes
– Bremermann, Rogson, et al.
- 1966
|
|
17
|
Decision boundary feature extraction for neural networks
– Lee, Landgrebe
- 1997
|
|
14
|
Adaptive fuzzy rule-based classification systems
– Nozaki, Ishibuchi, et al.
- 1996
|
|
13
|
Parsimonious network design and feature selection through node pruning
– Mao, Mohiuddin, et al.
|
|
12
|
An Empirical Comparison of
– Weiss, Kapouleas
- 1989
|
|
11
|
Simultaneous feature scaling and selection using a genetic algorithm
– Raymer, Punch, et al.
- 1997
|
|
10
|
Simulation of biological evolution and machine learning: I. selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing
– Reed, Toombs, et al.
- 1967
|
|
9
|
Predicting conserved water-mediated and polar ligand interactions in proteins using a k-nearest-neighbors genetic algorithm
– Raymer, Sanschagrin, et al.
- 1997
|
|
7
|
Pattern recognition using discriminative feature extraction
– Biem, Katagiri, et al.
- 1997
|
|
7
|
A User's Guide to GAUCSD. In
– Schraudolph, Grefenstette
- 1992
|
|
7
|
The assessment of laboratory tests in the diagnosis of acute appendicitis
– Marchand, Lente, et al.
- 1983
|
|
6
|
The role of structure in antibody cross-reactivity between peptides and folded proteins
– Craig, Sanschagrin, et al.
- 1998
|
|
4
|
Atomic and residue hydrophilicity in the context of folded protein structures,” Proteins: Str
– Kuhn, Swanson, et al.
- 1995
|
|
3
|
Identifying the determinants of favorable solvation sites," Protein Engng, submitted
– Raymer, Holstius, et al.
|
|
2
|
Computers in the study of evolution
– Crosby
- 1967
|
