A Hybrid Algorithm for Text Classification Problem

This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines (SVM) with elitist genetic algorithm (GA). The new algorithm uses EGA, which is based on elite survival strategy, to optimize the parameters of SVM. Iris dataset and one hundred pieces of news reports in Chinese news are chosen to compare EGA-SVM, GA-SVM and traditional SVM. The results of numerical experiments show that EGA-SVM can improve classification performance effectively than the other algorithms. This text classification algorithm can be extended easily to apply to literatures in the field of electrical engineering. Streszczenie. W artykule przedstawiono nowy algorytm klasyfikacji tekstu bazujący na mechanizmie SVM (support vector machine) I algorytmie genetycznym. Algorytm zbadano na podstawie bazy danych Iris i setek innych chinskich przykladow. Algorytm wykazal swoją skutecznośc. Moze byc on latwo rozszerzony na analize tekstow w inzynierii elektrycznej (Hybrydowy algorytm do klasyfikacji tekstu)

[1]  Tom V. Mathew.  Genetic Algorithm , 2022 .

[2]  Dong Seong Kim,et al.  Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization , 2008, IEICE Trans. Inf. Syst..

[3]  Shian-Chang Huang,et al.  Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting , 2008, Expert Syst. Appl..

[4]  Shutao Li,et al.  Gene selection using genetic algorithm and support vectors machines , 2008, Soft Comput..

[5]  Xia Sheng-ping.  Web Page Classification Method Based on RSOM-Bayes , 2008 .

[6]  Shuang Liu,et al.  A new weighted support vector machine with GA-based parameter selection , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[7]  Ángel Fernando Kuri Morales,et al.  Evolutionary Training of SVM for Multiple Category Classification Problems with Self-adaptive Parameters , 2006, IBERAMIA-SBIA.

[8]  William Stafford Noble,et al.  Support vector machine , 2013 .

[9]  Liu Fang,et al.  A pattern classification method based on GA and SVM , 2002, 6th International Conference on Signal Processing, 2002..

[10]  Xuefeng Bruce Ling,et al.  Multiclass cancer classification and biomarker discovery using GA-based algorithms , 2005, Bioinform..

[11]  Massimo Melucci,et al.  Vector Space Model , 2019, Syntactic n-grams in Computational Linguistics.

[12]  Ming Lin,et al.  Support Vector Machine Optimized with Genetic Algorithm for Short-Term Load Forecasting , 2008, 2008 International Symposium on Knowledge Acquisition and Modeling.

[13]  Wei Sun,et al.  Evaluation of Competitiveness of Power Plants Based on Optimized SVM Using GA and AIS , 2008, 2008 International Conference on Risk Management & Engineering Management.

[14]  Ling Liu,et al.  Combination of Genetic Algorithm and Support Vector Machine for Daily Flow Forecasting , 2008, 2008 Fourth International Conference on Natural Computation.

[15]  Wen Luo.  Study on the Web Classification Based on Proximal Support Vector Machine , 2008 .

[16]  Tristan Fletcher,et al.  Support Vector Machines Explained , 2008 .

[17]  HuangShian-Chang,et al.  Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting , 2008 .

[18]  Xing-Ming Zhao,et al.  A Novel Hybrid GA/SVM System for Protein Sequences Classification , 2004, IDEAL.

[19]  Ngoc-Tu Nguyen,et al.  An Application of Support Vector Machines for Induction Motor Fault Diagnosis with Using Genetic Algorithm , 2008, ICIC.

[20]  Song Han-tao.  Application Research of Support Vector Machine in Web Information Classification , 2007 .

[21]  Abhisek Ukil,et al.  Support Vector Machine , 2007 .

[22]  Li Tao.  A Rough Set Method for Web Classification , 2003 .