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Author: search.filters.author.Jaiyen, SaichonSubject: search.filters.subject.Adaboost

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    Edited audio detection using ensemble learning
    (2015-02-27)
    Suwan, Takdanai
    ;
    Jaiyen, Saichon
    ;
    Wiangsripanawan, Rungrat
    Detecting edited audios is the challenging problem that can help forensic scientists to separate genuine, unedited recording from edited recordings. This paper proposes the technique for detecting edited audios using Ensemble Learning. This problem can be considered as a two-class classification problem which audio data are classified into two classes including edited and unedited audios. The performance of the proposed model is compared with the performance from the Support Vector Machine, Naïve Bayes, Radial Basis Function Neural Network, and Probabilistic Neural Networks. The experimental results demonstrate that the proposed model is the most appropriated method for detecting the edited audios.
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    A hybrid ensemble of machine and statistical learning using confidence-based boosting
    (2015-01-01)
    Chairatanasongporn, Nattawut
    ;
    Jaiyen, Saichon
    Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers can solve classification problems in variant data set. Moreover, combining the confidence value to the current boosting method can improve the performance of classification. The performance of proposed method is compared to the ensemble of decision trees and MRN created by Adaboost.M1 on data sets from UCI. The experimental results show that the proposed method can improve the accuracy in both binary and multiclass classification problems.