{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T12:09:31Z","timestamp":1762603771502},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2012,6]]},"abstract":"<jats:p>For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system.<\/jats:p>","DOI":"10.1142\/s0218001412500085","type":"journal-article","created":{"date-parts":[[2012,6,18]],"date-time":"2012-06-18T03:21:16Z","timestamp":1339989676000},"page":"1250008","source":"Crossref","is-referenced-by-count":13,"title":["STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING"],"prefix":"10.1142","volume":"26","author":[{"given":"TUNG-HUNG","family":"CHUEH","sequence":"first","affiliation":[{"name":"Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu 310, Taiwan, R. O. C."}]},{"given":"TAI-BEEN","family":"CHEN","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Sciences, I-Shou University Kaohsiung 824, Taiwan, R. O. C."}]},{"given":"HENRY HORNG-SHING","family":"LU","sequence":"additional","affiliation":[{"name":"Institute of Statistics, National Chiao Tung University, Hsinchu 300, Taiwan, R. O. C."}]},{"given":"SHAN-SHAN","family":"JU","sequence":"additional","affiliation":[{"name":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C."}]},{"given":"TEH-HO","family":"TAO","sequence":"additional","affiliation":[{"name":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C."}]},{"given":"JIUNN-HAUR","family":"SHAW","sequence":"additional","affiliation":[{"name":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C."}]}],"member":"219","published-online":{"date-parts":[[2012,10,16]]},"reference":[{"key":"rf1","first-page":"37","volume":"6","author":"Aha D. W.","journal-title":"Mach. Learn."},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013215010749"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.03.017"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(02)00052-3"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(02)00079-X"},{"key":"rf10","volume-title":"Experiments in Induction","author":"Hunt E.","year":"1966"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3502-4"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1162\/089976601300014493"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1007\/BF02344719"},{"key":"rf15","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.2517-6161.1988.tb01721.x","volume":"50","author":"Lauritzen S.","journal-title":"J. Roy. Stat. Soc. S. B"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.2307\/2347628"},{"key":"rf17","first-page":"1563","volume":"16","author":"Littlewort G.","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1007\/s10111-003-0143-x"},{"key":"rf19","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6393(03)00099-2"},{"key":"rf20","volume-title":"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference","author":"Pearl J.","year":"1988"},{"key":"rf21","doi-asserted-by":"publisher","DOI":"10.1109\/34.954607"},{"key":"rf22","volume-title":"C4.5: Programs for Machine Learning","author":"Quinlan J.","year":"1993"},{"key":"rf23","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-006-0025-y"},{"key":"rf24","volume-title":"Statistical Learning Theory","author":"Vapnik V. N.","year":"1998"},{"key":"rf25","doi-asserted-by":"publisher","DOI":"10.1109\/34.506414"},{"key":"rf26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.06.007"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001412500085","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T16:34:26Z","timestamp":1714062866000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001412500085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,6]]},"references-count":21,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,10,16]]},"published-print":{"date-parts":[[2012,6]]}},"alternative-id":["10.1142\/S0218001412500085"],"URL":"https:\/\/doi.org\/10.1142\/s0218001412500085","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,6]]}}}