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ÇѱÛÁ¦¸ñ(Korean Title) |
Áö¿ª Àü¹®°¡ÀÇ ¾Ó»óºí ÇнÀ |
¿µ¹®Á¦¸ñ(English Title) |
Ensemble Learning ofRegional Experts |
ÀúÀÚ(Author) |
À̺´¿ì
¾çÁöÈÆ
±è¼±È£
Byungwoo Lee
Yang jihoon
Seonho Kim
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 15 NO. 02 PP. 0135 ~ 0139 (2009. 02) |
Çѱ۳»¿ë (Korean Abstract) |
º» ³í¹®¿¡¼´Â Áö¿ª Àü¹®°¡¸¦ ÀÌ¿ëÇÑ »õ·Î¿î ¾Ó»óºí ¹æ¹ýÀ» Á¦½ÃÇÏ°íÀÚ ÇÑ´Ù. ÀÌ ¾Ó»óºí ¹æ¹ý¿¡¼´Â ÇнÀ µ¥ÀÌŸ¸¦ ºÐÇÒÇÏ¿© ¼Ó¼º °ø°£ÀÇ ¼·Î ´Ù¸¥ Áö¿ªÀ» ÀÌ¿ëÇÏ¿© Àü¹®°¡¸¦ ÇнÀ½ÃŲ´Ù. »õ·Î¿î µ¥ÀÌŸ¸¦ ºÐ·ùÇÒ ¶§¿¡´Â ±× µ¥ÀÌŸ°¡ ¼ÓÇÑ Áö¿ªÀ» ´ã´çÇÏ´Â Àü¹®°¡µé·Î °¡ÁßÄ¡ ÅõÇ¥¸¦ ÇÑ´Ù. UCI ±â°è ÇнÀ µ¥ÀÌŸ ÀúÀå¼Ò¿¡ ÀÖ´Â 10°³ÀÇ µ¥ÀÌŸ¸¦ ÀÌ¿ëÇÏ¿© ´ÜÀÏ ºÐ·ù±â, Bagging, Adaboost¿Í Á¤È®µµ¸¦ ºñ±³ÇÏ¿´´Ù. ÇнÀ ¾Ë°í¸®ÁòÀ¸·Î´Â SVM, Naive Bayes, C4.5¸¦ »ç¿ëÇÏ¿´´Ù. ±× °á°ú Áö¿ª Àü¹®°¡ÀÇ ¾Ó»óºí ÇнÀ ¹æ¹ýÀÌ C4.5¸¦ ÇнÀ ¾Ë°í¸®ÁòÀ¸·Î »ç¿ëÇÑ Bagging, Adaboost¿Í´Â ºñ½ÁÇÑ ¼º´ÉÀ» º¸¿´À¸¸ç ³ª¸ÓÁö ºÐ·ù±âº¸´Ù´Â ÁÁÀº ¼º´ÉÀ» º¸¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
We present a new ensemble learning method that employs the set of region experts, each of which learns to handle a subset of the training data. We split the training data and generate experts for different regions in the feature space. When classifying a data, we apply a weighted voting among the experts that include the data in their region. We used ten datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as Bagging and Adaboost. We used SMO, Naive Bayes and C4.5 as base learning algorithms. As a result, we found that the performance of our method is comparable to that of Adaboost and Bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods. |
Å°¿öµå(Keyword) |
¾Ó»óºí ÇнÀ
Ensemble Learning
ºÎ½ºÆÃ
Boosting
¹è±ë
Bagging
Áö¿ª Àü¹®°¡
Region Experts
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ÆÄÀÏ÷ºÎ |
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