scholarly journals Multiclass Classification with Cross Entropy-Support Vector Machines

2015 ◽  
Vol 72 ◽  
pp. 345-352 ◽  
Author(s):  
Budi Santosa
Keyword(s):  
Support Vector Machines ◽  
Multiclass Classification ◽  
Cross Entropy ◽  
Support Vector ◽  
Vector Machines

MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines

2018 ◽  
Vol 13 (2) ◽  
pp. 18-29 ◽  
Author(s):  
Alejandro Rosales-Perez ◽  
Salvador Garcia ◽  
Hugo Terashima-Marin ◽  
Carlos A. Coello Coello ◽  
Francisco Herrera
Keyword(s):  
Support Vector Machines ◽  
Multiclass Classification ◽  
Support Vector ◽  
Cooperative Evolution ◽  
Vector Machines

scholarly journals CROSS ENTROPY UNTUK OPTIMASI LAGRANGE MULTIPLIERS PADA SUPPORT VECTOR MACHINES SEBAGAI MODEL PREDIKSI FINANCIAL DISTRESS

Heuristic ◽  
2016 ◽  
Vol 13 (02) ◽  
Author(s):  
Herlina .
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Lagrange Multipliers ◽  
Financial Distress ◽  
Computing Time ◽  
Cross Entropy ◽  
Computational Time ◽  
Support Vector ◽  
Manufacturing Companies ◽  
Vector Machines

The competence in predicting financial distress becomes an important research due tothe advantage in preventing companies financial failure. Besides, financial distressprediction model will give benefit to the investors and creditors. This research developa financial distress prediction model for listed manufacturing companies in Indonesiausing Support Vector Machines (SVM). Mathematically, SVM is formulated in the formof quadratic programming, which requires high computational time in finding theoptimal solution. In this research, Cross Entropy (CE) is used to optimize one of theSVM’s parameter that is Lagrange multipliers to find the optimal solution or nearoptimal solution of dual Lagrange SVM. The accuracy of the prediction model andcomputation time will be compared between standard SVM and CE-SVM. Finally, notethat the CE-SVM can solve classification problems in computing time 9.7 times shorterthan the standard SVM with good accuracy results. Keywords: cross entropy, lagrange multipliers, support vector machines, financialdistress

Reordering Adaptive Directed Acyclic Graphs for Multiclass Support Vector Machines

2003 ◽  
Vol 7 (3) ◽  
pp. 315-321 ◽  
Author(s):  
Thimaporn Phetkaew ◽  
◽  
Wanchai Rivepiboon ◽  
Boonserm Kijsirikul
Keyword(s):  
Support Vector Machines ◽  
Multiclass Classification ◽  
Directed Acyclic Graphs ◽  
Support Vector ◽  
Svm Classifier ◽  
Optimal Sequence ◽  
Ongoing Research ◽  
Acyclic Graphs ◽  
Vector Machines ◽  
Number Of Classes

The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the Adaptive Directed Acyclic Graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm-Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, Reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We then compare our performance with previous methods including the ADAG and the Max Wins algorithm. Experimental results demonstrate that our method gives higher accuracy. Moreover it runs faster than Max Wins, especially when the number of classes and/or the number of dimensions are relatively large.

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