Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An introduction to variable and feature selection
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A Character Recognition Application of an Iterative Procedure for Feature Selection
IEEE Transactions on Computers
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Combining Single Class Features for Improving Performance of a Two Stage Classifier
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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In the framework of handwriting recognition, we present a novel GA-based feature selection algorithm in which feature subsets are evaluated by means of a specifically devised separability index. This index measures statistical properties of the feature subset and does not depends on any specific classification scheme. The proposed index represents an extension of the Fisher Linear Discriminant method and uses covariance matrices for estimating how class probability distributions are spread out in the considered N-dimensional feature space. A key property of our approach is that it does not require any a priori knowledge about the number of features to be used in the feature subset. Experiments have been performed by using three standard databases of handwritten digits and a standard database of handwritten letters, while the solutions found have been tested with different classification methods. The results have been compared with those obtained by using the whole feature set and with those obtained by using standard feature selection algorithms. The comparison outcomes confirmed the effectiveness of our approach.