An immune-inspired instance selection mechanism for supervised classification |
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Authors: | Grazziela P Figueredo Nelson F F Ebecken Douglas A Augusto Helio J C Barbosa |
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Institution: | 1. Federal University of Rio de Janeiro-COPPE, Rio de Janeiro, Brazil 2. LNCC-MCT, Rio de Janeiro, Brazil
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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. |
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