Journal Article10.1007/S11432-009-0126-5
Regularized multiple criteria linear programs for classification
TL;DR: A regularized multiple criteria linear program (RMCLP) is proposed for two classes of classification problems and experimental results show that both ORMCLP and RMCLP perform well.
read more
Abstract: Although multiple criteria mathematical program (MCMP), as an alternative method of classification, has been used in various real-life data mining problems, its mathematical structure of solvability is still challengeable. This paper proposes a regularized multiple criteria linear program (RMCLP) for two classes of classification problems. It first adds some regularization terms in the objective function of the known multiple criteria linear program (MCLP) model for possible existence of solution. Then the paper describes the mathematical framework of the solvability. Finally, a series of experimental tests are conducted to illustrate the performance of the proposed RMCLP with the existing methods: MCLP, multiple criteria quadratic program (MCQP), and support vector machine (SVM). The results of four publicly available datasets and a real-life credit dataset all show that RMCLP is a competitive method in classification. Furthermore, this paper explores an ordinal RMCLP (ORMCLP) model for ordinal multi-group problems. Comparing ORMCLP with traditional methods such as One-Against-One, One-Against-The rest on large-scale credit card dataset, experimental results show that both ORMCLP and RMCLP perform well.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Quality Credit Evaluation based on TOPSIS: Evidence from Air-conditioning Market in China
Xiaoqian Zhu,Fei Wang,Changzhi Liang,Jianping Li,Xiaolei Sun +4 more
- 01 Jan 2012
TL;DR: The discussion of evaluation results proves the feasibility and effectiveness of TOPSIS and reflects that indicators affect the evaluation results significantly.
37
Building socioemotional environments in metaverses for virtual teams in healthcare: a conceptual exploration
Xiaodan Yu,Dawn Owens,Deepak Khazanchi +2 more
- 08 Apr 2012
TL;DR: A conceptual model for understanding how metaverses enable the development of social-emotional environments in virtual teams is developed, which has the potential to facilitate effective collaboration and knowledge sharing invirtual teams.
23
Multi-view dimensionality reduction based on Universum learning
TL;DR: This paper extends CCA with Universum learning for multi-view data and the proposed method is termed as Universum CCA (UCCA), which aims to find basis vectors in multiple views to ensure that correlations between projections of target data are mutually maximized but correlations between predictions of Universum data and target data mutually minimized.
22
Lightly trained support vector data description for novelty detection
TL;DR: This paper proposes a novel low-complexity anomaly detection algorithm based on Support Vector Data Description that reduces the complexity by avoiding the calculation of Lagrange multipliers of an objective function, and locates an approximate pre-image of the SVDD sphere's center, within the input space itself.
11
Regularized Multiple Criteria Linear Programming via Linear Programming
Zhiquan Qi,Yingjie Tian,Yong Shi +2 more
- 01 Jan 2012
TL;DR: Numerical experiments show that the proposed novel RMCLP via linear programming (LP) for pattern classification (called LP-RMCLP) is comparable to traditional RM CLP in accuracy, however considerably faster than it.
8
References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Advances in kernel methods: support vector learning
Bernhard Schölkopf,Christopher John Burges,Alexander J. Smola +2 more
- 08 Feb 1999
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
7.3K
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
J. C. Platt
- 01 Jan 1999
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
5.7K
•Book
Fast training of support vector machines using sequential minimal optimization
John Platt
- 08 Feb 1999
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.