{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:24:57Z","timestamp":1781108697508,"version":"3.54.1"},"reference-count":18,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,7,1]]},"abstract":"<p>As an emerging human-computer interaction (HCI) technology, recognition of human hand gesture is considered a very powerful means for human intention reading. To construct a system with a reliable and robust hand gesture recognition algorithm, it is necessary to resolve several major difficulties of hand gesture recognition, such as inter-person variation, intra-person variation, and false positive error caused by meaningless hand gestures. This paper proposes a learning algorithm and also a classification technique, based on multivariate fuzzy decision tree (MFDT). Efficient control of a fuzzified decision boundary in the MFDT leads to reduction of intra-person variation, while proper selection of a user dependent (UD) recognition model contributes to minimization of inter-person variation. The proposed method is tested first by using two benchmark data sets in UCI Machine Learning Repository and then by a hand gesture data set obtained from 10 people for 15 days. The experimental results show a discernibly enhanced classification performance as well as user adaptation capability of the proposed algorithm.<\/p>","DOI":"10.4018\/ijfsa.2011070102","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T12:21:32Z","timestamp":1319026892000},"page":"15-31","source":"Crossref","is-referenced-by-count":8,"title":["Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation"],"prefix":"10.4018","volume":"1","author":[{"given":"Moon-Jin","family":"Jeon","sequence":"first","affiliation":[{"name":"Korea Aerospace Research Institute, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Wan","family":"Lee","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeungnam","family":"Bien","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijfsa.2011070102-0","author":"E.Alpaydin","year":"2004","journal-title":"Introduction to machine learning"},{"key":"ijfsa.2011070102-1","unstructured":"Bien, Z., Park, K.-H., Bang, W.-C., & Stefanov, D. 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Unpublished doctoral dissertation, Korea Advanced Institute of Science and Technology, Daejeon, Korea."},{"key":"ijfsa.2011070102-11","unstructured":"Merz, C. J., & Murphy, P. M. (1996). UCI repository for machine learning data-bases. Retrieved from http:\/\/archive.ics.uci.edu\/ml\/"},{"key":"ijfsa.2011070102-12","doi-asserted-by":"crossref","unstructured":"Nam, Y., & Wohn, K. (1996). Recognition of. space-time hand-gesture using hidden Markov model. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology (pp. 51-58).","DOI":"10.1145\/3304181.3304193"},{"key":"ijfsa.2011070102-13","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"issue":"2","key":"ijfsa.2011070102-14","first-page":"276","article-title":"A fuzzy rule-based approach to spatio-temporal hand gesture recognition. IEEE Transactions on Systems, Man, and Cybernetics","volume":"30","author":"M. 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