The nature of statistical learning theory
The nature of statistical learning theory
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Ant colony optimization theory: a survey
Theoretical Computer Science
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
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Considering fault diagnosis is a small sample problem in real chemical process industry, Support Vector Machines (SVM) is adopted as classifier to discriminate chemical process steady faults. To improve fault diagnosis performance, it is essential to reduce the dimensionality of collected data. This paper presents a modified discrete binary ant colony optimization (MDBACO) to optimize discrete combinational problems, and then further combines it with SVM to accomplishing fault feature selection. The tests of optimizing benchmark functions show the developed MDBACO is valid and effective. The fault diagnosis results and comparisons of simulations based on Tennessee Eastman Process (TEP) prove the feature selection method based on MDBACO and SVM can find the essential fault variables quickly and exactly, and greatly increases the fault diagnosis correct rates as irrelevant variables are eliminated properly.