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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
SVM ±â¹Ý Bagging°ú OoD Ž»öÀ» È°¿ëÇÑ Á¦Á¶°øÁ¤ÀÇ ºÒ±ÕÇü Dataset¿¡ ´ëÇÑ ¿¹Ãø¸ðµ¨ÀÇ ¼º´ÉÇâ»ó |
¿µ¹®Á¦¸ñ(English Title) |
Boosting the Performance of the Predictive Model on the Imbalanced Dataset Using SVM Based Bagging and Out-of-Distribution Detection |
ÀúÀÚ(Author) |
Sadriddinov Ilkhomjon Rovshan Ugli
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Sadriddinov Ilkhomjon Rovshan Ugli
Doo-Soon Park
±èÁ¾ÈÆ
¿ÀÇÏ¿µ
Jong Hoon Kim
Hayoung Oh
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¿ø¹®¼ö·Ïó(Citation) |
VOL 11 NO. 11 PP. 0455 ~ 0464 (2022. 11) |
Çѱ۳»¿ë (Korean Abstract) |
Á¦Á¶¾÷ÀÇ °øÁ¤¿¡¼ »ý¼ºµÇ´Â µ¥ÀÌÅͼÂÀº Å©°Ô µÎ °¡Áö Ư¡À» °¡Áø´Ù. Ÿ°Ù Ŭ·¡½ºÀÇ ½É°¢ÇÑ ºÒ±ÕÇü°ú Áö¼ÓÀûÀÎ Out-of-Distribution(OoD) »ùÇÃÀÇ ¹ß»ýÀÌ´Ù. Ŭ·¡½º ºÒ±ÕÇüÀº SMOTE ¹× ´Ù¾çÇÑ »ùÇøµ Àü·«À» ÅëÇؼ ´ëÀÀÇÒ ¼ö ÀÖ´Ù. ±×·¯³ª, OoD Ž»öÀº ÇöÀç±îÁö Àΰø½Å°æ¸Á ¿µ¿ª¿¡¼¸¸ ´Ù·ïÁ® ¿Ô´Ù. OoD Ž»öÀÇ Àû¿ëÀÌ °¡´ÉÇÑ Àΰø½Å°æ¸ÁÀº Á¦Á¶°øÁ¤ µ¥ÀÌÅͼ¿¡ ´ëÇؼ ¸¸Á·½º·¯¿î ¼º´ÉÀ» ¹ßÇöÇÏÁö ¸øÇÑ´Ù. ¿øÀÎÀº Á¦Á¶°øÁ¤ÀÇ µ¥ÀÌÅÍ ¼ÂÀÌ Àΰø½Å°æ¸Á¿¡¼ ÀϹÝÀûÀ¸·Î ´Ù·ç´Â À̹ÌÁö, ÅؽºÆ® µ¥ÀÌÅͼ°ú ºñ±³Çؼ Å©±â°¡ ¸Å¿ì ÀÛ°í, ³ëÀÌÁî°¡ ½ÉÇÏ´Ù´Â °ÍÀÌ´Ù. ¶ÇÇÑ Àΰø½Å°æ¸ÁÀÇ °úÀûÇÕ(overfitting) ¹®Á¦µµ Á¦Á¶¾÷ µ¥ÀÌÅͼ¿¡¼ Àΰø½Å°æ¸ÁÀÇ ¼º´ÉÀ» ÀúÇÏÇÏ´Â ¿øÀÎÀ¸·Î ÁöÀûµÈ´Ù. ÀÌ¿¡ ÇöÀç±îÁö ½ÃµµµÈ ¹Ù ¾ø´Â SVM ¾Ë°í¸®Áò°ú OoD Ž»öÀÇ Á¢¸ñÀ» ½ÃµµÇÏ¿´´Ù. ¶ÇÇÑ ¿¹Ãø¸ðµ¨ÀÇ Á¤¹Ðµµ Çâ»óÀ» À§ÇØ ¹è±ë(Bagging) ¾Ë°í¸®ÁòÀ» ¸ðµ¨¸µ¿¡ ¹Ý¿µÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model. |
Å°¿öµå(Keyword) |
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Recommendation System
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Out-of-Distribution(OoD) Ž»ö
Imbalanced Dataset
Predictive Performance
Bagging
Out-of-Distribution(OoD) Detection
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