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A new streaming learning for stream chunk data classification based on incremental learning and adaptive boosting algorithm
(2018-01-01)
Claypo, Niphat
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Hanskunatai, Anantaporn
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Jaiyen, Saichon
Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. This paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. The proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. This learning method concentrates on learning from sequential chunks of data stream. The distribution weight updating method uses error of previous hypothesis to update the weight. The learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. The experimental results show that the accuracy of the proposed method is higher than other methods in all datasets.
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Edited audio detection using ensemble learning
(2015-02-27)
Suwan, Takdanai
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Jaiyen, Saichon
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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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SEMG signal classification using SMO algorithm and singular value decomposition
(2015-01-01)
Ruangpaisarn, Yotsapat
;
Jaiyen, Saichon
Surface Electromyography (sEMG) signal analysis is a challenging task in neuroscience. The signal is associated with an activity of muscles in Human body. It is a part of how human can control the robotic arm for helping people with disabilities. In this paper, we propose a new method based on Singular Value Decomposition (SVD) and SMO algorithm for classifying sEMG signals into six basic hand movements. By this proposed method, SVD is adopted for feature extraction and SMO classifier is used for classifying sEMG signals into six classes of basic hand movements in five subjects. In preliminary experiment, we investigates the number of features that can yield the best performance in the classification and it is found that the optimal number of features is 50. For performance evaluation, five classifiers including Decision Tree, K-nearest neighbor, Naive Bayes, RBF, and SMO, with 10 fold cross-validation technique are adopted. The experimental results have shown that SMO algorithm with V2M-SVD feature extraction can achieve the best performance for the classification of basic hand movements.
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Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection
(2014-01-01)
Claypo, Niphat
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Jaiyen, Saichon
Currently, Thai restaurants are popular around the world. There are tons of reviews related to foods and services in social networking websites. These tons of customer reviews make it difficult to analyze the opinions of customer toward foods and services. To help the businesses, the model of opinion mining is proposed for classifying the reviews and to analyze the attitude of customers for improving their products and services. In this research, the artificial neural network is applied to classify the positive and negative reviews. In addition, the mRMR feature selection is used to select the features of data in order to reduce the number of features in the data set. Consequently, the computational times of learning algorithms for neural networks are reduced. The experimental results show that the neural network is an effective model for classifying the Thai restaurant reviews.
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One-pass-throw-away learning algorithm based on hybridization of LDA and PCA
(2013-09-17)
Thakong, Mongkhon
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Phimoltares, Suphakant
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Jaiyen, Saichon
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Lursinsap, Chidchanok
This paper proposes a new learning algorithm based on the versatile elliptic basis function (VEBF) by considering only the most data distributions for automatic computing the appropriate width vector. In addition, the orthonormal basis and Linear Discriminant Analysis (LDA) technique are also applied to the proposed method for adjusting the directions of the hyperellipsoid in the network and improving the performance. After tested by real world data sets, the proposed method illustrates that it outperforms the VEBF and other learning algorithms. © 2013 IEEE.

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