A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-05
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
The efficiency of a binary support vector machine- (SVM-) based classifier depends on the combination and the number of input features extracted from raw signals.
Sometimes, a combination of individual good features does not perform well in discriminating a class due to a high level of relevance to a second class also.
Moreover, an increase in the dimensions of an input vector also degrades the performance of a classifier in most cases.
To get efficient results, it is needed to input a combination of the lowest possible number of discriminating features to a classifier.
In this paper, we propose a framework to improve the performance of an SVM-based classifier for sensor fault classification in two ways: firstly, by selecting the best combination of features for a target class from a feature pool and, secondly, by minimizing the dimensionality of input vectors.
To obtain the best combination of features, we propose a novel feature selection algorithm that selects m out of M features having the maximum mutual information (or relevance) with a target class and the minimum mutual information with nontarget classes.
This technique ensures to select the features sensitive to the target class exclusively.
Furthermore, we propose a diversified-input SVM (DI-SVM) model for multiclass classification problems to achieve our second objective which is to reduce the dimensions of the input vector.
In this model, the number of SVM-based classifiers is the same as the number of classes in the dataset.
However, each classifier is fed with a unique combination of features selected by a feature selection scheme for a target class.
The efficiency of the proposed feature selection algorithm is shown by comparing the results obtained from experiments performed with and without feature selection.
Furthermore, the experimental results in terms of accuracy, receiver operating characteristics (ROC), and the area under the ROC curve (AUC-ROC) show that the proposed DI-SVM model outperforms the conventional model of SVM, the neural network, and the k-nearest neighbor algorithm for sensor fault detection and classification.
American Psychological Association (APA)
Jan, Sana Ullah& Koo, Insoo. 2018. A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors،Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1202021
Modern Language Association (MLA)
Jan, Sana Ullah& Koo, Insoo. A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors No. 2018 (2018), pp.1-21.
https://search.emarefa.net/detail/BIM-1202021
American Medical Association (AMA)
Jan, Sana Ullah& Koo, Insoo. A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1202021
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202021