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Author: search.filters.author.Claypo, NiphatSubject: search.filters.subject.Classification

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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
    ;
    Hanskunatai, Anantaporn
    ;
    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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    Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection
    (2014-01-01)
    Claypo, Niphat
    ;
    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.