Power System Database Feature Selection Using a Relaxed Perceptron Paradigm
Feature selection has become a relevant and challenging problem for the area of knowledge discovery in database. An effective feature selection strategy can significantly reduce the data mining processing time, improve the predicted accuracy, and help to understand the induced models, as they tend to be smaller and make more sense to the user. In this paper, an effective research around the utilization of the Perceptron paradigm as a method for feature selection is carried out. The idea is training a Perceptron and then utilizing the interconnection weights as indicators of which attributes could be the most relevant. We assume that an interconnection weight close to zero indicates that the associated attribute to this weight can be eliminated because it does not contribute with relevant information in the construction of the class separator hyper-plane. The experiments were realized with 4 real and 11 synthetic databases. The results show that the proposed algorithm is a good trade-off among performance (generalization accuracy), efficiency (processing time) and feature reduction. Specifically, we apply the algorithm to a Mexican Electrical Billing database with satisfactory accuracy, efficiency and feature reduction results.
KeywordsSupport Vector Machine Feature Selection Feature Selection Method Synthetic Dataset Feature Reduction
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