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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ

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Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Ŭ·¡½º ¿µ¿ªÀ» º¸Á¸ÇÏ´Â ÃÊ¿ù »ç°¢Çü¿¡ ÀÇÇÑ ÇÁ·ÎÅäŸÀÔ ¼±Åà ¾Ë°í¸®Áò
¿µ¹®Á¦¸ñ(English Title) Hyper-Rectangle Based Prototype Selection Algorithm Preserving Class Regions
ÀúÀÚ(Author) ¹éº´Çö   ¾î¼ºÀ²   ȲµÎ¼º   Byunghyun Baek   Seongyul Euh   Doosung Hwang  
¿ø¹®¼ö·Ïó(Citation) VOL 09 NO. 03 PP. 0083 ~ 0090 (2020. 03)
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(Korean Abstract)
ÇÁ·ÎÅäŸÀÔ ¼±ÅÃÀº ÈÆ·Ã µ¥ÀÌÅͷκÎÅÍ Å¬·¡½º ¿µ¿ªÀ» ´ëÇ¥ÇÏ´Â ÃÖ¼Ò µ¥ÀÌÅ͸¦ ¼±ÅÃÇÏ¿© ³·Àº ÇнÀ ½Ã°£ ¹× ÀúÀå °ø°£À» º¸ÀåÇÏ´Â ÀåÁ¡À» Á¦°øÇÑ´Ù. º» ³í¹®Àº ¸ðµç ºÐ·ù ¾Ë°í¸®Áò¿¡ Àû¿ëÇÒ ¼ö ÀÖ´Â ÃÊ¿ù »ç°¢ÇüÀ» ÀÌ¿ëÇÑ »õ·Î¿î ÈÆ·Ã µ¥ÀÌÅÍÀÇ »ý¼º ¹æ¹ýÀ» ¼³°èÇÑ´Ù. ÃÊ¿ù »ç°¢Çü ¿µ¿ªÀº ¼­·Î ´Ù¸¥ Ŭ·¡½º µ¥ÀÌÅ͸¦ Æ÷ÇÔÇÏÁö ¾ÊÀ¸¸ç Ŭ·¡½º °ø°£À» ºÐÇÒÇÑ´Ù. ¼±ÅÃµÈ ÃÊ¿ù »ç°¢Çü ³» µ¥ÀÌÅÍÀÇ Áß°£°ªÀº ÇÁ·ÎÅäŸÀÔÀÌ µÇ¾î »õ·Î¿î ÈÆ·Ã µ¥ÀÌÅ͸¦ ±¸¼ºÇÏ°í, ÃÊ¿ù »ç°¢ÇüÀÇ Å©±â´Â Ŭ·¡½º ¿µ¿ªÀÇ µ¥ÀÌÅÍ ºÐÆ÷¸¦ ¹Ý¿µÇÏ¿© Á¶ÀýµÈ´Ù. Àüü ÈÆ·Ã µ¥ÀÌÅ͸¦ ´ëÇ¥ÇÏ´Â ÃÖ¼ÒÀÇ ÇÁ·ÎÅäŸÀÔ ÁýÇÕ ¼±ÅÃÀ» À§ÇØ ÁýÇÕ µ¤°³ ÃÖÀûÈ­ ¾Ë°í¸®ÁòÀ» ¼³°èÇß´Ù. Á¦¾ÈÇÏ´Â ¹æ¹ý¿¡¼­´Â Ž¿å ¾Ë°í¸®Áò°ú °ö¼À ¿¬»êÀ» Æ÷ÇÔÇÏÁö ¾ÊÀº °Å¸® °è»ê½ÄÀ» ÀÌ¿ëÇÏ¿© ÁýÇÕ µ¤°³ ÃÖÀûÈ­ ¾Ë°í¸®ÁòÀÇ ´ÙÇ× ½Ã°£À» ¿ä±¸ÇÏ´Â ½Ã°£ º¹Àâµµ ¹®Á¦¸¦ ÇØ°áÇÑ´Ù. ½ÇÇè¿¡¼­´Â ºÐ·ù ¼º´ÉÀÇ ºñ±³¸¦ À§ÇØ ÃÖ±ÙÁ¢ ÀÌ¿ô ±ÔÄ¢°ú ÀÇ»ç °áÁ¤ Æ®¸® ¾Ë°í¸®ÁòÀ» ÀÌ¿ëÇϸç Á¦¾ÈÇÏ´Â ¹æ¹ýÀÌ ÃÊ¿ù ±¸¸¦ ÀÌ¿ëÇÑ ÇÁ·ÎÅäŸÀÔ ¼±Åà ¹æ¹ýº¸´Ù ¿ì¼öÇÏ´Ù.
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(English Abstract)
Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance
Å°¿öµå(Keyword) Prototype Selection   Prototype   Hyper-Rectangle   Set Cover Optimization Algorithm   ÇÁ·ÎÅäŸÀÔ ¼±Åà  ÇÁ·ÎÅäŸÀÔ   ÃÊ¿ù »ç°¢Çü   ÁýÇÕ µ¤°³ ÃÖÀûÈ­ ¾Ë°í¸®Áò  
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