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Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
Current Result Document :
2
/ 6
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ÇѱÛÁ¦¸ñ(Korean Title)
¸íÁ¦ÈµÈ ¾îÆ®¸®ºäÆ® Åüҳë¹Ì¸¦ ÀÌ¿ëÇÏ´Â ³ªÀÌºê º£À̽º ÇнÀ ¾Ë°í¸®Áò
¿µ¹®Á¦¸ñ(English Title)
Propositionalized Attribute Taxonomy Guided Naive Bayes Learning Algorithm
ÀúÀÚ(Author)
°´ë±â
Â÷°æȯ
Dae-Ki Kang
Kyung-Hwan Cha
¿ø¹®¼ö·Ïó(Citation)
VOL 12 NO. 12 PP. 2357 ~ 2364 (2008. 12)
Çѱ۳»¿ë
(Korean Abstract)
º» ³í¹®¿¡¼´Â ¸íÁ¦ÈµÈ ¾îÆ®¸®ºäÆ® Åüҳë¹Ì¸¦ ÀÌ¿ëÇÏ¿© °£°áÇÏ°í °°ÇÇÑ ºÐ·ù±â¸¦ »ý¼ºÇÏ´Â ¹®Á¦¸¦ °í·ÁÇÑ´Ù. ÀÌ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇØ ¸íÁ¦ÈµÈ ¾îÆ®¸®ºäÆ® Åüҳë¹Ì(Propositionalized Attribute Taxonomy)¸¦ ÀÌ¿ëÇÏ´Â ³ªÀÌºê º£À̽º ÇнÀ ¾Ë°í¸®Áò(Naive Bayes Learner)ÀÎ PAT-NBLÀ» ¼Ò°³ÇÑ´Ù. PAT-NBLÀº ¸íÁ¦ÈµÈ ¾îÆ®¸®ºäÆ®µéÀÇ Åüҳë¹Ì¸¦ ¼±Çè Áö½ÄÀ¸·Î ÀÌ¿ëÇÏ¿© °£°áÇÏ°í Á¤È®ÇÑ ºÐ·ù±â¸¦ ±Í³³ÀûÀ¸·Î ÇнÀÇÏ´Â ¾Ë°í¸®ÁòÀÌ´Ù. PAT-NBLÀº ÁÖ¾îÁø Åüҳë¹Ì¿¡¼ Áö¿ªÀûÀ¸·Î ÃÖÀûÀÇ ÄÆ(cut)À» ã¾Æ³»±â À§ÇØ ÇÏÇâ½Ä Ž»ö°ú »óÇâ½Ä Ž»öÀ» »ç¿ëÇÑ´Ù. ã¾Æ³½ ÃÖÀûÀÇ ÄÆÀº ¸íÁ¦ÈµÈ ¾îÆ®¸®ºäÆ® Åüҳë¹Ì¿Í µ¥ÀÌÅͷκÎÅÍ ±×¿¡ »óÀÀÇÏ´Â ÀνºÅϽº °ø°£(instance space)À» ±¸¼ºÇÒ ¼ö ÀÖ°Ô ÇØÁØ´Ù. University of California-Irvine (UCI) ÀúÀå¼ÒÀÇ ±â°èÇнÀ º¥Ä¡¸¶Å© µ¥ÀÌÅÍ¿¡ ´ëÇÑ ½ÇÇè °á°ú¸¦ º¸¸é, Á¦¾ÈµÈ ¾Ë°í¸®ÁòÀÌ Ç¥ÁØÀûÀÎ ³ªÀÌºê º£À̽º ÇнÀ ¾Ë°í¸®Áò¿¡ ÀÇÇØ ¸¸µé¾îÁø ºÐ·ù±âµé°ú ºñ±³ÇØ º¼ ¶§, °¡²ûÀº º¸´Ù °£°áÇÏ°í ´õ Á¤È®ÇÑ ºÐ·ù±â¸¦ »ý¼ºÇØ ³½´Ù´Â »ç½ÇÀ» ¾Ë ¼ö ÀÖ¾ú´Ù.
¿µ¹®³»¿ë
(English Abstract)
In this paper, we consider the problem of exploiting a taxonomy of propositionalized attributes in order to generate compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.
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