Please use this identifier to cite or link to this item: http://repositorio.ufes.br/handle/10/9872
Title: Classifier Ensemble Feature Selection for Automatic Fault Diagnosis
metadata.dc.creator: BOLDT, F. A.
Issue Date: 14-Jul-2017
Publisher: Universidade Federal do Espírito Santo
Citation: BOLDT, F. A., Classifier Ensemble Feature Selection for Automatic Fault Diagnosis
Abstract: "An efficient ensemble feature selection scheme applied for fault diagnosis is proposed, based on three hypothesis: a. A fault diagnosis system does not need to be restricted to a single feature extraction model, on the contrary, it should use as many feature models as possible, since the extracted features are potentially discriminative and the feature pooling is subsequently reduced with feature selection; b. The feature selection process can be accelerated, without loss of classification performance, combining feature selection methods, in a way that faster and weaker methods reduce the number of potentially non-discriminative features, sending to slower and stronger methods a filtered smaller feature set; c. The optimal feature set for a multi-class problem might be different for each pair of classes. Therefore, the feature selection should be done using an one versus one scheme, even when multi-class classifiers are used. However, since the number of classifiers grows exponentially to the number of the classes, expensive techniques like Error-Correcting Output Codes (ECOC) might have a prohibitive computational cost for large datasets. Thus, a fast one versus one approach must be used to alleviate such a computational demand. These three hypothesis are corroborated by experiments. The main hypothesis of this work is that using these three approaches together is possible to improve significantly the classification performance of a classifier to identify conditions in industrial processes. Experiments have shown such an improvement for the 1-NN classifier in industrial processes used as case study."
URI: http://repositorio.ufes.br/handle/10/9872
Appears in Collections:PPGI - Teses de doutorado

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