A Regularized Monotonic Fuzzy Support Vector Machine Model for Data Mining With Prior Knowledge

Sheng-Tun Li, Chih Chuan Chen

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Incorporating prior knowledge into data mining is an interesting but challenging problem, and this study proposes a novel fuzzy support vector machine (SVM) model to explore this issue. It considers the fact that in many applications, each input point may not be exactly labeled as one particular class, and thus, it applies a fuzzy membership to each input point. It also utilizes expert knowledge concerning the monotonic relations between the response and predictor variables, which is represented in the form of monotonicity constraints. We formulate the classification problem of a monotonically constrained fuzzy SVM, called a monotonic FSVM, derive its dual optimization problem, and theoretically analyze its monotonic property. The Tikhonov regularization method is further applied to ensure that the solution is unique and bounded. A new measure, i.e., the frequency monotonicity rate, is proposed to evaluate the ability of the model to retain the monotonicity. The results of the experiments on real-world and synthetic datasets show that this method, which considers different contributions of each data and the prior knowledge of the monotonicity, has a number of advantages with regard to predictive ability and retaining monotonicity over the original FSVM and SVM models when applied to classification problems.

Original languageEnglish
Article number6965607
Pages (from-to)1713-1727
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Volume23
Issue number5
DOIs
Publication statusPublished - 2015 Oct 1

Fingerprint

Prior Knowledge
Monotonic
Support vector machines
Data mining
Monotonicity
Support Vector Machine
Data Mining
Classification Problems
Fuzzy Membership
Model
Tikhonov Regularization
Dual Problem
Regularization Method
Predictors
Optimization Problem
Experiments
Evaluate
Experiment

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Cite this

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A Regularized Monotonic Fuzzy Support Vector Machine Model for Data Mining With Prior Knowledge. / Li, Sheng-Tun; Chen, Chih Chuan.

In: IEEE Transactions on Fuzzy Systems, Vol. 23, No. 5, 6965607, 01.10.2015, p. 1713-1727.

Research output: Contribution to journalArticle

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