A Hybrid Algorithm for Text Classification Problem

• Autorzy: Liu X. , Fu H.

Warianty tytułu

PL

Hybrydowy algorytm do klasyfikacji tekstu

• Języki publikacji: EN

Abstrakty

EN

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.

PL

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 chińskich przykładów. Algorytm wykazał swoją skuteczność. Może być on łatwo rozszerzony na analizę tekstów w inżynierii elektrycznej.

Słowa kluczowe

EN

genetic algorithm; support vector machine; text classification

PL

algorytm genetyczny; SVM; klasyfikacja tekstu

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 1b
• Strony: 8--11
• Opis fizyczny: Bibliogr. 22 poz., schem., tab.

Twórcy

autor

Liu X.

autor

Fu H.

• Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, 510665, China,

Bibliografia

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