Noise Clipping Algorithm Based on Relative Contribution Rate

doi:10.16182/j.issn1004731x.joss.19-FZ0289

Abstract

Abstract: This paper presents a class noise cutting algorithm (Class noise cutting, CNC) based on relative contribution rate. The algorithm calculates the relative contribution rate of features to the theme. The most valuable feature set is selected by using features distinguish rating. The corresponding candidate categories for each feature are selected, to reduece the candidate category set, improves the classification accuracy, and speed up the response speed of the classifier. Compared with another ECN noise cutting algorithm (Eliminating the class whose), CNC-has higher accuracy and because of its simpler feature dimension dictionary and better candidate category set, the response speed is greatly accelerated.

Key words: relative contribution rate, class noise cutting, hierarchical classification, feature selection

CLC Number:

TP278

Liu Shuoyu, Dai Yueming. Noise Clipping Algorithm Based on Relative Contribution Rate[J]. Journal of System Simulation, 2019, 31(12): 2721-2730.

References

[1] Du Y P, Liu J X, Ke W M, et al.Hierarchy Construction and Text Classfication Based on the Relaxation Strategy and Least Information Model[J]. Expert Systems with Applications (S0957-4174), 2018, 100: 157-164.
[2] 何力, 贾焰, 韩伟红. 大规模层次分类问题研究及其进展[J]. 计算机学报, 2012, 35(10): 2101-2115.
He Li, Jia Yan, Han Weihong.Category Candiadte Search in Large Scale Hierarchical Classification[J]. Chinese Journal of Computers, 2012, 35(10): 2101-2115.
[3] Sun A X, Lim E P.Hierarchical text classification and evaluation. Proceedings of the 2001 IEEE International Conference on Data Mining[C]. California, USA. 2001: 521-528.
[4] Liu T Y, Yang Y M, Wan H, et al.Support vector machines classification with a very large-scale taxonomy[J]. ACM SIGKDD Explorations Newsletter (S1931-0145), 2005, 7(2): 36-43.
[5] Silva-Palacios D, Ferri C.Improving Performance of Multiclass Classfications By Including Class Hierarchies[J]. Procedia Computer Science (S1877-0509), 2017, 108: 1692-1701.
[6] Luengo J, Shim S O, Alshomrani S, et al.CNC-NOS: Class Noise Cleaing By Ensemble Filtering and Noise Scoring[J]. Knowledge-Based Systems (S0950-7051), 2018, 140: 27-49.
[7] Zhuge H, He L.Automatic Maintenance of Category Hierarchy[J]. Future Generation Computer Systems (S0167-739X), 2017, 67: 1-12.
[8] Pereira R B, Plastino A, Zadrozny B, et al.Correlation Analysis of Performance Measure for Multi-label Classfication[J]. Information Processing & Management (S0306-4573), 2018, 54(3): 359-369.
[9] Kumar V, Pujari A K, Padmanabhan V, et al.Multi-label Classfication using Hierarchical Embedding[J]. Expert Systems with Applications (S0957-4174), 2018, 91: 263-269.
[10] Truică C O, Darmont J, Boicea A, et al.Benchmarking Top-k Keyword and Top-k Document Processing With T²K² and T²K²D²[J]. Future Generation Computer Systems (S0167-739X), 2018, 85: 60-75.
[11] Jiang L X, Wang S S, Li C Q, et al.Structure Extended Multinomial Naive Bayes[J]. Information Science (S0020-0255), 2016, 329: 346-356.
[12] Zhang L G, Jiang L X, Li C Qun, et al.Two Features Weighting Approachers For Naive Bayes Text Classfiers[J]. Knowledge-Based Systems (S0950-7051), 2016, 100: 137-144.
[13] Zhang D, Jiao L C, Bai X, et al.A Robust Semi-supervised SVM Via Ensemble Learning[J]. Applied Soft Computing (S1568-4946), 2018, 65: 632-643.
[14] Gaudioso M, Gorgone E, Labbé M, et al.Lagrangian Relaxation For SVM Feature Selection[J]. Computers & Operation Research (S0305-0548), 2017, 87: 137-145.
[15] Rempała G A, esołowski J. Double Asymptotics for the Chi-square Statistic[J]. Statistics & Probability Letters (S0167-7152), 2016, 119: 317-325.
[16] Ikram S T, Cherukuri A K.Intrusion Detection Model Using Fusion of Chi-square Feature Selection and Multi Class SVM[J]. Journal of King Saud University - Computer and Information Science (S1319-1578), 2017, 29(4): 462-472.
[17] Jadhav S, He H M, Jenkins K.Information Gain Directed Genetic Algorithm Wrapper Feature Selection For Credit Rating[J]. Applied Soft Computing (S1568-4946), 2018, 69: 541-553.
[18] Bennasar M, Hicks Y, Setchi R.Feature Selection Using Joint Mutual Information Maximisation[J]. Expert Systems with Applications (S0957-4174), 2015, 42(22): 8520-8532.
[19] 王强, 关毅, 王晓龙. 基于特征类别属性分析的文本分类器分类噪声裁减算法[J]. 自动化学报, 2007, 33(8): 809-816.
Wang Qiang, Guan Yi, Wang Xiaolong.A Method for Eliminating Class Noise In Text Classification Based on Feature Class Attribute[J]. Acta Automatica Sinica, 2007, 33(8): 809-816.