Hanskunatai, Anantaporn

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Hanskunatai, Anantaporn
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Hanskunatai, Anantapom
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anantaporn.ha@kmitl.ac.th

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    A new hybrid intelligent system for fast neural network training
    (2013-01-01)
    Hanskunatai, Anantaporn
    A major drawback of artificial neural network is long training time depending on a number of training data. Thus, the contribution of this work is to present the intelligent hybrid system for faster training on neural network. The concept of the proposed method is applying DBSCAN for removing noise and outliers then selecting the represented instances to form a smaller training set for further model training. The experimental results indicate that the proposed method can dramatically reduce a size of training set while the predictive performance of the classifiers are better or almost the same as models trained with original training sets. © 2013 Springer-Verlag Berlin Heidelberg.
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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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    A comparative investigation of PSG signal patterns to classify sleep disorders using machine learning techniques
    (2015-01-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Patients with Non-Communicable Diseases (NCDs) are increasing around the globe. Possible causes of the NCDs are continuously being investigated. One of them is a sleep disorder. In order to detect specific sleep disorders, the Polysomnography (PSG), is necessary. However, due to the lack of the PSG in many hospitals, researchers attempt to discover alternative approaches. This article demonstrates comparisons of sleep disorder classifications using machine learning techniques. Three main machine learning techniques have been compared including Classification And Regression Tree (CART), k-Mean Clustering (KMC) and Support Vector Machine (SVM). The SVM achieves the best classification results in NREM-1 and NREM-2. The CART performs superior in NREM-3 and REM. Implications in terms of medical diagnosis, there are two main selected features, SaO2 and Pulse, based on the CART in all of the sleep stages. The features may be pieces of evidences to predict various types of sleep disorders.
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    The ensemble of Naïve Bayes classifiers for hotel searching
    (2014-01-01)
    Srisuan, Janasthar
    ;
    Hanskunatai, Anantaporn
    The objective of the paper is to present a new ensemble of Naïve Bayes classifiers model for an application of hotel searching. The dataset were collected from 293 reviews of 15 hotels in Phuket. The main idea of the proposed model is to combine two models of Naïve Bayes classifiers with different feature selection techniques. The output of the searching model is a list of hotel names ranking by hotel probability related to user keywords. The searching performance of the ensemble model was compared with two classical searching methods: Boolean searching and Boyer-Moore searching. The results show that the ensemble of Naïve Bayes classifiers model provides the highest average rank-accuracy. In addition, the proposed model also takes the fastest time in searching when compared with the other techniques.
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    A MULTI-LAYER HYBRID MACHINE LEARNING MODEL for AUTOMATIC SLEEP STAGE CLASSIFICATION
    (2018-12-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying W, R and N1, N2, N3 in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694±0.22 of accuracy (AUC=0.822±0.31) in subject-specific classification and 0.942±0.02 of accuracy (AUC=0.920±0.17) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.
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    Auto-Tuning of parameters in hybrid sampling method for class imbalance problem
    (2017-02-21)
    Sanguanmak, Yotsathon
    ;
    Hanskunatai, Anantaporn
    The class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic parameter tuning for the DBSM algorithm by using a genetic algorithm (GA), called GADBSM. The experimental results of GADBSM are compared with the DBSM algorithm. The results show that the GADBSM can enhance the classification performance of the DBSM algorithm. Moreover, the GADBSM provides the best in F-measure and AUC in all datasets.
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    A New Hybrid Sampling Approach for Classification of Imbalanced Datasets
    (2018-09-11)
    Hanskunatai, Anantaporn
    Nowadays it is an era of data driven. Many organizations around the world including bank, industry, commercial, and medical intend to extract knowledge from a huge of data. But in the real-word datasets, most of them occur class imbalance problems. This paper presents a new algorithm to handle an imbalanced classification. The proposed technique is a hybrid sampling approach which is the combination of a well know oversampling algorithm called SMOTE and the undersampling technique by removing the ambiguous instances from the majority class instances. The experimental results show that the new hybrid sampling method yields the better predictive performance in term of F-measure when compare with other sampling techniques. In addition, it can improve f-measure up to 59.73% and 412.26% when compare with the original dataset based on decision tree learning and naïve bayes classifiers respectively.
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    DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification
    (2016-11-18)
    Sanguanmak, Yotsathon
    ;
    Hanskunatai, Anantaporn
    Many applications in the real world encounter the class imbalance problem. This problem affects the performance of the model prediction. Nowadays, resampling technique is a popular technique to handle the class imbalance problem such as oversampling, undersampling, and hybridsampling. Thus, this paper proposes a new hybrid resampling technique to deal with the class imbalance problem, called DBSM. The concept of DBSM is to use DBSCAN algorithm for undersampling and apply SMOTE technique for oversampling. The experimental results of the DBSM algorithm are compared with an original datasets and other sampling techniques, which are SMOTE, Tomek Links, SMOTE+Tomek Links and DBSCAN. The results show that the DBSM can improve the predictive performance of the classifiers. In addition, it yields the best in the average of AUC, F-measure, and accuracy.