Regularized multiple-criteria linear programming with universum and its application

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Universum-regularized multiple-criteria linear programming (called $${\mathfrak{U}}$$-RMCLP) was proposed and firstly applied to railway safety field, which is useful extension of RMCLP. Experiments in public datasets show that $${\mathfrak{U}}$$-RMCLP can get better results than its original model. Furthermore, experiment results in the trouble of moving freight car detection system (TFDS) datasets indicate that the accuracy of $${\mathfrak{U}}$$-RMCLP has been up to 91 %, which will provide great help for TFDS system.

[1]  Wuyang Dai,et al.  Practical Conditions for Effectiveness of the Universum Learning , 2011, IEEE Transactions on Neural Networks.

[2]  F. Glover,et al.  Simple but powerful goal programming models for discriminant problems , 1981 .

[3]  Yong Shi,et al.  Multi-instance classification based on regularized multiple criteria linear programming , 2012, Neural Computing and Applications.

[4]  Changshui Zhang,et al.  Selecting Informative Universum Sample for Semi-Supervised Learning , 2009, IJCAI.

[5]  Yanchun Zhang,et al.  Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring , 2010, IEEE Transactions on Knowledge and Data Engineering.

[6]  Yong Shi,et al.  Robust twin support vector machine for pattern classification , 2013, Pattern Recognit..

[7]  Yi Peng,et al.  Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming , 2005, Ann. Oper. Res..

[8]  David L. Olson,et al.  Introduction to Business Data Mining , 2005 .

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Yong Shi,et al.  Laplacian twin support vector machine for semi-supervised classification , 2012, Neural Networks.

[11]  Yi Peng,et al.  Data Mining via Multiple Criteria Linear Programming: Applications in Credit Card Portfolio Management , 2002, Int. J. Inf. Technol. Decis. Mak..

[12]  Yong Shi,et al.  Twin support vector machine with Universum data , 2012, Neural Networks.

[13]  Xiaojun Chen,et al.  A Multiple-Criteria Quadratic Programming Approach to Network Intrusion Detection , 2004, CASDMKM.

[14]  Yong Shi,et al.  Structural Regular Multiple Criteria Linear Programming for Classification Problem , 2014, Int. J. Comput. Commun. Control.

[15]  Fumin Shen,et al.  {\cal U}Boost: Boosting with the Universum , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ned Freed,et al.  EVALUATING ALTERNATIVE LINEAR PROGRAMMING MODELS TO SOLVE THE TWO-GROUP DISCRIMINANT PROBLEM , 1986 .

[17]  Marc Toussaint,et al.  Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.

[18]  Gang Kou,et al.  Bankruptcy prediction for Japanese firms: using Multiple Criteria Linear Programming data mining approach , 2006, Int. J. Bus. Intell. Data Min..

[19]  Yong Shi,et al.  Regular Multiple Criteria Linear Programming for Semi-supervised Classification , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[20]  Xiaojun Chen,et al.  Regularized multiple criteria linear programs for classification , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[21]  Gang Kou,et al.  Classification of HIV-I-Mediated neuronal dendritic and synaptic damage using multiple criteria linear programming , 2007, Neuroinformatics.

[22]  Zhiquan Qi,et al.  Regularized multiple-criteria linear programming via second order cone programming formulations , 2012, DM-IKM '12.

[23]  Yong Shi,et al.  Regularized Multiple Criteria Linear Programming via Linear Programming , 2012, ICCS.

[24]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[25]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[26]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[27]  Fei Wang,et al.  Semi-Supervised Classification with Universum , 2008, SDM.

[28]  Olvi L. Mangasarian,et al.  Generalized Support Vector Machines , 1998 .

[29]  Jason Weston,et al.  Inference with the Universum , 2006, ICML.