Hierarchical feature selection based on relative dependency for gear fault diagnosis

Mariela Cerrada, René Vinicio Sánchez, Fannia Pacheco, Diego Cabrera, Grover Zurita, Chuan Li

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

© 2015, Springer Science+Business Media New York. Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the entire set. Consequently, algorithms to identify these features are valuable contributions. This work deals with the feature selection problem through attribute clustering. The proposed algorithm is inspired by existing approaches, where the relative dependency between attributes is used to calculate dissimilarity values. The centroids of the created clusters are selected as representative attributes. The selection algorithm uses a random process for proposing centroid candidates, in this way, the inherent exploration in random search is included. A hierarchical procedure is proposed for implementing this algorithm. In each level of the hierarchy, the entire set of available attributes is split in disjoint sets and the selection process is applied on each subset. Once the significant attributes are proposed for each subset, a new set of available attributes is created and the selection process runs again in the next level. The hierarchical implementation aims to refine the search space in each level on a reduced set of selected attributes, while the computational time-consumption is improved also. The approach is tested with real data collected from a test bed, results show that the diagnosis precision by using a Random Forest based classifier is over 98 % with only 12 % of the attributes from the available set.
Translated title of the contributionSelección jerárquica de características basada en la dependencia relativa para el diagnóstico de fallas del engranaje
Original languageEnglish (US)
Pages (from-to)687-703
Number of pages17
JournalApplied Intelligence
DOIs
StatePublished - 1 Apr 2016

Fingerprint

Dive into the research topics of 'Hierarchical feature selection based on relative dependency for gear fault diagnosis'. Together they form a unique fingerprint.

Cite this