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Current Result Document :
5
/ 5
ÀÌÀü°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
ÀÇ¹Ì ±â¹Ý À¯Àü ¾Ë°í¸®ÁòÀ» »ç¿ëÇÑ Æ¯Â¡ ¼±ÅÃ
¿µ¹®Á¦¸ñ(English Title)
Semantic-based Genetic Algorithm for Feature Selection
ÀúÀÚ(Author)
±èÁ¤È£
ÀÎÁÖÈ£
ä¼öȯ
Jung-ho Kim
Joo-ho In
Soo-hoan Chae
¿ø¹®¼ö·Ïó(Citation)
VOL 13 NO. 04 PP. 0001 ~ 0010 (2012. 08)
Çѱ۳»¿ë
(Korean Abstract)
º» ³í¹®Àº ¹®¼ ºÐ·ùÀÇ Àüó¸® ´Ü°èÀΠƯ¡ ¼±ÅÃÀ» À§ÇØ Àǹ̸¦ °í·ÁÇÑ ÃÖÀûÀÇ Æ¯Â¡ ¼±Åà ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. Ư¡ ¼±ÅÃÀº ºÒÇÊ¿äÇÑ Æ¯Â¡À» Á¦°ÅÇÏ°í ºÐ·ù¿¡ ÇÊ¿äÇÑ Æ¯Â¡À» ÃßÃâÇÏ´Â ÀÛ¾÷À¸·Î ºÐ·ù ÀÛ¾÷¿¡¼ ¸Å¿ì Áß¿äÇÑ ¿ªÇÒÀ» ÇÑ´Ù. Ư¡ ¼±Åà ±â¹ýÀ¸·Î Ư¡ÀÇ Àǹ̸¦ ÆľÇÇÏ¿© Ư¡À» ¼±ÅÃÇÏ´Â LSA(Latent Semantic Analysis) ±â¹ýÀ» »ç¿ëÇÏÁö¸¸ ±âº» LSA´Â ºÐ·ù ÀÛ¾÷¿¡ Ư¼ºÈ µÈ ±â¹ýÀÌ ¾Æ´Ï¹Ç·Î ÁöµµÀû ÇнÀÀ» ÅëÇØ ºÐ·ù¿¡ ÀûÇÕÇϵµ·Ï °³¼±µÈ ÁöµµÀû LSA¸¦ »ç¿ëÇÑ´Ù. ÁöµµÀû LSA¸¦ ÅëÇØ ¼±ÅÃµÈ Æ¯Â¡µé·ÎºÎÅÍ ÃÖÀûÈ ±â¹ýÀÎ À¯Àü ¾Ë°í¸®ÁòÀ» »ç¿ëÇÏ¿© ´õ ÃÖÀûÀÇ Æ¯Â¡µéÀ» ÃßÃâÇÑ´Ù. ¸¶Áö¸·À¸·Î, ÃßÃâÇÑ Æ¯Â¡µé·Î ºÐ·ùÇÒ ¹®¼¸¦ Ç¥ÇöÇÏ°í SVM (Support Vector Machine)À» ÀÌ¿ëÇÑ Æ¯Á¤ ºÐ·ù±â¸¦ »ç¿ëÇÏ¿© ºÐ·ù¸¦ ¼öÇàÇÏ¿´´Ù. ÁöµµÀû LSA¸¦ ÅëÇØ Àǹ̸¦ °í·ÁÇÏ°í À¯Àü ¾Ë°í¸®ÁòÀ» ÅëÇØ ÃÖÀûÀÇ Æ¯Â¡ ÁýÇÕÀ» ãÀ½À¸·Î½á ³ôÀº ºÐ·ù ¼º´É°ú È¿À²¼ºÀ» º¸ÀÏ °ÍÀÌ¶ó °¡Á¤ÇÏ¿´´Ù. ÀÎÅÍ³Ý ´º½º ±â»ç¸¦ ´ë»óÀ¸·Î ºÐ·ù ½ÇÇèÀ» ¼öÇàÇÑ °á°ú ÀûÀº ¼öÀÇ Æ¯Â¡µé·Î ³ôÀº ºÐ·ù ¼º´ÉÀ» È®ÀÎÇÒ ¼ö ÀÖ¾ú´Ù.
¿µ¹®³»¿ë
(English Abstract)
In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved through experiments using internet news classification with low features.
Å°¿öµå(Keyword)
ºÐ·ù(Classification)
Ư¡ ¼±ÅÃ(Feature Selection)
ÀáÀçÀû ÀÇ¹Ì ºÐ¼®(Latent Semantic Analysis)
À¯Àü ¾Ë°í¸®Áò(Genetic Algorithm)
¼Æ÷Æ® º¤ÅÍ ¸Ó½Å(Support Vector Machine)
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