Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
A Feature Selection Technique in the Neural Network for Demand Forecasting of Mobile Payment System |
ÀúÀÚ(Author) |
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Ho-Joon Kim
Yun-Seok Cho
Kyungmi Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 45 NO. 04 PP. 0370 ~ 0375 (2018. 04) |
Çѱ۳»¿ë (Korean Abstract) |
º» ³í¹®¿¡¼´Â ¸ð¹ÙÀÏ °áÁ¦½Ã½ºÅÛÀÇ ¼ºñ½º ¼ö¿ä¿¹ÃøÀ» À§ÇÑ ¹æ¹ý·ÐÀ¸·Î¼ ½Å°æ¸Á ±â¹ÝÀÇ ½Ã°è¿¿¹Ãø ±â¹ýÀ» Á¦½ÃÇÑ´Ù. ¿¹Ãø¿¡ ÇÊ¿äÇÑ Æ¯Â¡ ¼±º°°úÁ¤°ú ½Ã°è¿ µ¥ÀÌÅÍÀÇ ¿¹Ãø°úÁ¤À» À§ÇÏ¿© 2´Ü°è ½Å°æ¸Á ¸ðµ¨À» Á¦¾ÈÇÏ¸ç ±× µ¿ÀÛ Æ¯¼º°ú ¾Ë°í¸®Áò¿¡ °üÇØ ±â¼úÇÑ´Ù. Ư¡ µ¥ÀÌÅÍÀÇ Ç¥ÇöÀ» À§ÇÏ¿© 3Á¾·ùÀÇ ÆÛÁö ¸â¹ö½±ÇÔ¼ö¸¦ Àû¿ëÇϸç, ÇÏÀÌÆÛ¹Ú½º ±â¹ÝÀÇ ½Å°æ¸Á ¸ðµ¨À» »ç¿ëÇÏ¿© Ư¡ÀÇ ¿¬°üµµ ¿ä¼Ò¸¦ Æò°¡ÇÏ´Â ¹æ¹ýÀ» Á¦½ÃÇÑ´Ù. Á¦¾ÈµÈ Ư¡ ¼±º° ±â¹ýÀº ¿¹Ãø ½Ã½ºÅÛÀÇ °è»ê·®À» °¨¼Ò½ÃÅ°¸ç, ÇнÀµ¥ÀÌÅÍ ÁýÇÕ¿¡¼ ¿Ö°îµÈ Ư¡ µ¥ÀÌÅ͸¦ Á¦°ÅÇÒ ¼ö ÀÖ°Ô ÇÑ´Ù. ½ÇÁ¦ ½º¸¶Æ®Ä·ÆÛ½º ½Ã½ºÅÛ¿¡¼ ÃëµæÇÑ 2³â°£ÀÇ µ¥ÀÌÅ͸¦ »ç¿ëÇÏ¿© ½ÇÇèÀ» ¼öÇàÇÏ°í ±× °á°ú¸¦ ÅëÇÏ¿© Á¦¾ÈµÈ ±â¹ýÀÇ À¯¿ë¼ºÀ» Æò°¡ÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In this paper, we present a time series prediction technique based on neural network as a methodology for forecasting service demand of mobile payment system. We propose a two-stage neural network model for the feature selection process and the prediction process. Three types of fuzzy membership functions were adopted for the representation of feature data, and a hyperbox-based neural network model is used for the evaluation of feature relevance factor. The proposed feature selection technique reduces the amount of computation and eliminates erroneous feature data in the learning data set. We evaluated the usefulness of the proposed method through experiments using two years of data obtained form actual smart campus systems.
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Å°¿öµå(Keyword) |
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½Ã°è¿ ¿¹Ãø
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mobile payment system
time series prediction
neural networks
feature selection
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