Inequality distance hyperplane multiclass support vector machines
Pages 2046 - 2060
Abstract
In this study, inequality distance hyperplane multiclass support vector machines (IDH‐MSVM) algorithm is proposed on the basis of multiclassification support vector machine (MSVM) which was proposed by J. Weston and C. Watkins in 1999. It only needs to solve a single objective optimization problem to deal with multiclassification problems. For original MSVM, the hyperplane distance refers to the classification interval between classical margin and hyperplane, which is equality. However, the IDH‐MSVM introduces parameters to adjust distance between every classification hyperplane and classical margin, which makes the hyperplane distance inequality. The effectiveness of the proposed method is experimented on UCI standard data sets and compared with several multiclassification algorithms. Experimental results show that this method has a better classification effect on multiclassification data.
References
[1]
Pandey M, Rautaray SS. Machine Learning: Theoretical Foundations and Practical Applications. Economy of Region, Springer; 2017.
[2]
Zhang BC, Li C, Lin N. Machine Learning and Visual Perception. De Gruyter; 2020.
[3]
Volkmann N, Kulig B, Hoppe S, Stracke J, Hensel O, Kemper N. On‐farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning. J Dairy Sci. 2021;104(5):5921‐5931.
[4]
Taotao W, SoungChang L, Shengli Z. When blockchain meets AI: optimal mining strategy achieved by machine learning. Int J Intell Syst. 2021;36(5):2183‐2207.
[5]
Rehouma R, Buchert M, Chen YP. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. Int J Intell Syst. 2021;36(9):5085‐5115.
[6]
Kwon H, Oh KC, Choi Y, Chung YG, Kim J. Development and application of machine learning‐based prediction model for distillation column. Int J Intell Syst. 2021;36(5):1970‐1997.
[7]
Vapnik V. Statistical Learning Theory. John Wiley and Sons; 1998.
[8]
Nadia AR, NorAshidi MI, Mohammad KMD, Hazem AN. Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems. Int J Intell Syst. 2021;36(10):5605‐5669.
[9]
Jessen HC, Menard S. Applied logistic regression analysis. J Roy Stat Soc D‐Sta: Series D (The Statistician). 1996;45(4):534‐535. doi:10.2307/2988559
[10]
Liu J, Zhu K, Lu W, Luo X, Zhao X. A lightweight 3D convolutional neural network for deepfake detection. Int J Intell Syst. 2021;36(9):4990‐5004.
[11]
Reed R, MarksII RJ. Neural Smithing:Supervised Learning in Feedforward Artificial Neural Networks. The MIT Press; 2018.
[12]
Niell S, Jess F, Daz R, et al. Beehives biomonitor pesticides in agroecosystems: Simple chemical and biological indicators evaluation using Support Vector Machines (SVM). Eco Indic. 2018;91:149‐154.
[13]
Chen W, Yang H, Feng P, Ding H, Lin H. IDNA4mc: identifying DNAn4‐methylcytosine sites based on nucleotide chemical properties. Bioinformatics. 2017;33(22):3518‐3523.
[14]
Kodieswari A, Deepa D. Detection of distant lung cancer using enhanced transductive support vector machines. IIJITEE. 2020;9(4):672‐677.
[15]
Zhu YW, Yan QS, Lu JB. Fault diagnosis method for discslitting machine based on wavelet packet transform and support vector machine. Int J Comput Integ M. 2020;33(10‐11):1118‐1128.
[16]
Chen X, Qi X, Wang Z, Cui C, Wu B, Yan Y. Fault diagnosis of rolling bearing using marine predators algorithm‐based support vector machine and topology learning and out‐of‐sample embedding. Measurement. 2021;176:109‐116.
[17]
Ehrentraut C, Ekholm M, Tanushi H, Tiedemann J, Dalianis H. Detecting hospital‐acquired infections: A document classification approach using support vector machines and gradient tree boosting, Health.Inform.J. 2016;24(1):24‐42.
[18]
Viaene S, Baesens B, Van Gestel T, et al. Knowledge discovery in a direct marketing case using least squares support vector machines. Int J Intell Sys. 2001;16(9):1023‐1036.
[19]
Yu H, Sun C, Yang X, Zheng S, Zou H. Fuzzy support vector machine with relative density information for classifying imbalanced data. IEEE T Fuzzy Syst. 2019;27(12):2353‐2367.
[20]
Gupta D, Richhariya B, Borah P. A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural Comput Appl. 2019;31(11):7153‐7146.
[21]
Maldonado S, Merig J, Miranda J. Redefining support vector machines with the ordered weighted average. Knowl Based Syst. 2018;148:41‐46.
[22]
Merig J. A unified model between the weighted average and the induced owa operator. Expert Syst Appl. 2011;38(9):11560‐11572.
[23]
Maldonado S, Merig J, Miranda J. IOWA‐SVM: a density‐based weighting strategy for SVM classifification via OWA operators. IEEE T Fuzzy Syst. 2019;28(9):2143‐2150.
[24]
Maldonado S, López J, Vairetti C. Time‐weighted Fuzzy Support Vector Machines for classification in changing environments. Inform Sciences. 2012;559:97‐110.
[25]
Li CH, Yu J, Li S. Fuzzy support vector machine algorithm based on inequality hyper‐plane distance. Comp Syst Appl. 2020;29(10):185‐191.
[26]
Debnath R, Takahide N, Takahashi H. A decision based one‐against‐one method for multi‐class support vector machine. Pattern Anal Appl. 2004;7(2):164‐175.
[27]
Chen G, Zhu XF, Xu QQ, Xu AC, Yang H. Film defects of lithium battery recognition based on brightness and one‐against‐all support vector machine. Appl Mech Mater. 2013;462‐463:155‐158.
[28]
Sidaoui B, Sadouni K. Binary tree multi‐class SVM based on OVA approach and variable neighbourhood search algorithm. IJCAT. 2017;55(3):183‐190.
[29]
Weston J, Watkins C. Multi‐Class Support Vector Machines. Springer; 2005.
[30]
Crammer K, Singer Y. On the algorithmic implementation of multiclass kernel‐based vector machines. J Mach Learn Res. 2001;2(2):265‐292.
[31]
Yong Z, Chi Z, Yu S. A novel multi‐class support vector machine based on fuzzy theories. International Conference on Intelligent Computing, Springer, Berlin, Heidelberg. 2006;4113:43‐50.
[32]
Wang Z, Xue X. Multi‐Class Support Vector Machine. Springer International Publishing; 2014.
[33]
Li K, Li J. Structural fuzzy multi‐class support vector machine. Journal of Physics. Conference Series. 2020;1631(1):012188.
[34]
Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE T Neural Networks. 2002;13(2):415‐425.
[35]
UCI Machine Learning Repository; 2021. http://archive.ics.uci.edu/ml
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© 2021 Wiley Periodicals LLC.
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Published: 25 January 2022
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