TY - JOUR ID - 51656 TI - Using Curve Fitting in Error Correcting Output Codes JO - مجله علمی رایانش نرم و فناوری اطلاعات JA - JSCIT LA - fa SN - 2383-1006 AU - Haddadi, Maryam AU - Ahmadi, Maliheh AU - Keyvanpour, Mohammad Reza AU - Riahi, Noushin AD - Master of Artificial Intelligence, Computer Engineering Department, Alzahra University, Iran AD - Associative Professor, Computer Engineering Department, Alzahra University, Iran Y1 - 2017 PY - 2017 VL - 5 IS - 1 SP - 62 EP - 69 KW - classifier KW - Coding KW - Curve Fitting KW - Decoding KW - Error Correcting Output Codes (ECOC) DO - N2 - The Error Correcting Output Codes (ECOC) represent any number of the binary classifiers to model the multiclass problems successfully. In this paper, we have used Curve Fitting as a binary classifier in ECOC algorithm to solve multiclass classification problems. Curve Fitting is a classifier based on a nonlinear decision boundary that separates two pattern classes by the curves of the best fit, and arriving at optimal boundary points between two classes. Since we need a coding and a decoding strategy to design an ECOC system, this paper gives five coding and eight decoding strategies of ECOC and compares the results of Curve Fitting with Adaboost classification and Nearest Mean Classifier (NMC). This evaluation has been performed on different data sets of UCI machine learning repository. The results indicate that One-versus-one, ECOC-ONE coding and LAP, BDEN decoding having the best results in contrast with another coding and decoding strategies and Curve Fitting  is a good base classifier in ECOC, also it is comparable with the other ECOC approaches. UR - https://jscit.nit.ac.ir/article_51656.html L1 - https://jscit.nit.ac.ir/article_51656_e61f82a783bce7321d4583a14da756b8.pdf ER -