TY - JOUR ID - 332 TI - A novel hybrid method for vocal fold pathology diagnosis based on russian language JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Majidnezhad, V. AD - United Institute of Informatics Problems, National Academy of Science of Belarus Y1 - 2014 PY - 2014 VL - 2 IS - 2 SP - 141 EP - 147 KW - Ensemble of Decision Tree KW - Genetic Algorithm (GA) KW - Mel Frequency Cepstral Coefficients (MFCC) KW - Wavelet Packet Decomposition (WPD) KW - Vocal Fold Pathology Diagnosis DO - 10.22044/jadm.2014.332 N2 - In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%). UR - https://jad.shahroodut.ac.ir/article_332.html L1 - https://jad.shahroodut.ac.ir/article_332_77dd06395a7022f5b01c449aa547b995.pdf ER -