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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > 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 :

ÇѱÛÁ¦¸ñ(Korean Title) µ¶¼º °¨Áö¸¦ À§ÇÑ »ý¹° Á¶±â °æº¸ ½Ã½ºÅÛ
¿µ¹®Á¦¸ñ(English Title) Biological Early Warning System for Toxicity Detection
ÀúÀÚ(Author) ±è¼º¿ë   ±Ç±â¿ë   ÀÌ¿øµ·   Sung Yong Kim   Ki Yong Kwon   Won Don Lee  
¿ø¹®¼ö·Ïó(Citation) VOL 14 NO. 09 PP. 1979 ~ 1986 (2010. 09)
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(Korean Abstract)
»ý¹° Á¶±â °æº¸ ½Ã½ºÅÛÀº ¹°¼Ó »ý¸íüÀÇ ÇൿÀ» °üÂûÇÏ¿© µ¶¼ºÀ» °¨ÁöÇÑ´Ù. ÀÌ ½Ã½ºÅÛÀº ºÐ·ù±â¸¦ ¹°ÀÇ µ¶¼ºÀÇ À¯¹«¿Í Á¤µµ¸¦ ÆÇ´ÜÇϱâ À§ÇØ »ç¿ëÇÑ´Ù. ÀÌ ºÐ·ù±âÀÇ ¼º´ÉÀ» ³ôÀ̱â À§ÇØ Àû¿ëÇÒ ¼ö ÀÖ´Â ¹æ¹ý Áß¿¡ ºÎ½ºÆà ¾Ë°í¸®ÁòÀÌ ÀÖ´Ù. ºÎ½ºÆÃÀº ±âº» ºÐ·ù±â·Î´Â ¿¹Ãø Á¤È®µµ°¡ ³·¾Ò´ø ºÐ·ùÇϱ⠾î·Á¿î »ç°Ç¿¡ ÁýÁßÇÒ ¼ö ÀÖµµ·Ï ´ÙÀ½ ¹ø µ¥ÀÌÅÍ¿¡ ÇØ´ç ÈÆ·Ã »ç°Ç(event)µéÀÌ »ÌÈú È®·üÀ» ³ô¿©ÁØ´Ù. Ƚ¼ö°¡ ÁøÇàµÉ¼ö·Ï ºÐ·ù±â°¡ ¾î·Á¿î »ç°ÇµéÀ» ÁýÁßÀûÀ¸·Î °í·ÁÇÏ°Ô µÈ´Ù. ±× °á°ú ºÐ·ùÇϱ⠾î·Á¿ü´ø »ç°Ç¿¡ ´ëÇÑ ¿¹Ãø ¼º´ÉÀº ÁÁ¾ÆÁöÁö¸¸, ºñ±³Àû ½¬¿î ÈÆ·Ã »ç°ÇµéÀÇ Á¤º¸´Â ¹ö·ÁÁö´Â ´ÜÁ¡ÀÌ ÀÖ´Ù. º» ³í¹®¿¡¼­´Â ÀÌ °°Àº ´ÜÁ¡À» º¸¿ÏÇϱâ À§ÇØ ºÐ·ù±â¿¡ È®ÀåµÈ µ¥ÀÌÅÍ Ç¥ÇöÀ» À§ÇÑ Á¡ÁøÀû ÇнÀ¹ýÀÇ Àû¿ëÀ» Á¦¾ÈÇÑ´Ù. È®ÀåµÈ µ¥ÀÌÅÍ Ç¥ÇöÀÇ °¡ÁßÄ¡ º¯¼ö¸¦ »ç¿ëÇÏ¸é ¾àÇÏ°Ô ºÐ·ùµÇ´Â »ç°Ç »Ó ¾Æ´Ï¶ó ½±°Ô ºÐ·ùµÇ´Â »ç°ÇÀÇ Á¤º¸±îÁöµµ »ç¿ëÇÏ¿© ºÐ·ù±âÀÇ ¿¹Ãø Á¤È®µµ¸¦ ³ôÀÏ ¼ö ÀÖ°Ô µÈ´Ù. »õ·Î Àû¿ëµÈ ¾Ë°í¸®Áò°ú ±âÁ¸ÀÇ Áß¿äµµ º¯¼ö¸¦ »ç¿ëÇÏÁö ¾Ê´Â learn ¸¦ ºñ±³ÇÏ¿© ¼º´ÉÀÌ Çâ»óµÊÀ» °ËÁõÇÏ¿´´Ù.
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(English Abstract)
Biological early warning system detects toxicity by looking at behavior of organisms in water. The system uses classifier for judgement about existence and amount of toxicity in water. Boosting algorithm is one of possible application method for improving performance in a classifier. Boosting repetitively change training example set by focusing on difficult examples in basic classifier. As a result, prediction performance is improved for the events which are difficult to classify, but the information contained in the events which can be easily classified are discarded. In this paper, an incremental learning method to overcome this shortcoming is proposed by using the extended data expression. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression by exploiting the necessary information not only from the well classified, but also from the weakly classified events. Experimental results show that the new algorithm outperforms the former Learn method without using the weight parameter.
Å°¿öµå(Keyword) ºÎ½ºÆà  Á¡ÁøÀû ÇнÀ   ÀÇ»ç°áÁ¤ Æ®¸®   È®ÀåµÈ µ¥ÀÌÅÍ Ç¥Çö   ¿£Æ®·ÎÇÇ ÇÔ¼ö   Boosting   Incremental learning   Decision tree   Extended data expression   Entropy function  
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