Incorporating monotonic domain knowledge in support vector learning for data mining regression problems

Abstract

A common problem of data-driven data mining methods is that they might lack considering domain knowledge, despite possibly having high accuracy with respect to the data. As such, prior knowledge plays an important role in many data mining applications. Incorporating prior knowledge into data mining techniques is not trivial and remains a partially open issue drawing much attention. In this paper, we propose a new support vector regression (SVR) model that takes into account the prior knowledge of domain experts in the form of inequalities, which reflect the monotonic relationship between the output and some of the attributes of the input. A dual quadratic programming problem corresponding to the SVR model is derived, along with algorithms for solving it and creating constraints, respectively. The experiment results, which were conducted on two artificial and two practical datasets, show that the proposed model, which considers the monotonicity defined by domain experts, performs better than the original SVR. Moreover, the proposed method is also suitable for prior domain knowledge of piecewise monotonicity.

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Acknowledgements

This study was supported in part by the Ministry of Science and Technology, Taiwan, under contract NSC 102-2410-H-006-080-MY3, MOST 105-2410-H-006-038-MY3 and MOST 107-2410-H-143-005. The authors also thank Mr. Chi Chou and Mr. Yu-Di, Chen for their help with the experimentation.

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Correspondence to Sheng-Tun Li.

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Chuang, HC., Chen, CC. & Li, ST. Incorporating monotonic domain knowledge in support vector learning for data mining regression problems. Neural Comput & Applic 32, 11791–11805 (2020). https://doi.org/10.1007/s00521-019-04661-4

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Keywords

  • Data mining
  • Monotonicity constraint
  • Support vector regression
  • Prior domain knowledge