An immune-inspired instance selection mechanism for supervised classification

Abstract

One issue in data classification problems is to find an optimal subset of instances to train a classifier. Training sets that represent well the characteristics of each class have better chances to build a successful predictor. There are cases where data are redundant or take large amounts of computing time in the learning process. To overcome this issue, instance selection techniques have been proposed. These techniques remove examples from the data set so that classifiers are built faster and, in some cases, with better accuracy. Some of these techniques are based on nearest neighbors, ordered removal, random sampling and evolutionary methods. The weaknesses of these methods generally involve lack of accuracy, overfitting, lack of robustness when the data set size increases and high complexity. This work proposes a simple and fast immune-inspired suppressive algorithm for instance selection, called SeleSup. According to self-regulation mechanisms, those cells unable to neutralize danger tend to disappear from the organism. Therefore, by analogy, data not relevant to the learning of a classifier are eliminated from the training process. The proposed method was compared with three important instance selection algorithms on a number of data sets. The experiments showed that our mechanism substantially reduces the data set size and is accurate and robust, specially on larger data sets.