Classifier Ensemble Feature Selection for Automatic Fault Diagnosis

Name: Francisco de Assis Boldt
Type: PhD thesis
Publication date: 14/07/2017

Namesort ascending Role
Thomas Walter Rauber Advisor *

Examining board:

Namesort ascending Role
Thomas Walter Rauber Advisor *
Flávio Miguel Varejão Co advisor *

Summary: "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."

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