Fast Feature Selection for Handwritten Digit Recognition

Feature selection happens to be an important step in any classification process. Its aim is to reduce the number of features and at the same time to try to maintain or even improve the performance of the used classifier. Variability of handwriting makes features more or less efficient and gives a good support for evaluation of selection method. The selection methods described in the literature present some limitations at different levels. Some are too complex or too dependent on the classifier used for evaluation. Others overlook interactions between features. In this paper, we propose a fast selection method based on a genetic algorithm. Each feature is closely associated with a single feature classifier. The weak classifiers we consider have several degrees of freedom and are optimized on the training dataset. The classifier subsets are evaluated by a fitness function based on a combination of single feature classifiers. Results on the MNIST handwritten digits database show how robust our approach is and how efficient the method is.