Papers by Thimaporn Phetkaew
Journal of Information Science Theory and Practice, 2020
This research aimed (1) to study and analyze the ability of current information retrieval (IR) sy... more This research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an indepth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users' cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users.
This research developed and evaluated a software development support method to help non-expert de... more This research developed and evaluated a software development support method to help non-expert developers evaluating or gathering requirements and designing or evaluating digital technology solutions to accessibility barriers people with visual impairment encounter. The Technology Enhanced Interaction Framework (TEIF) Visual Impairment (VI) Method was developed through literature review and interviews with 20 students with visual impairment, 10 adults with visual impairment and five accessibility experts. It is an extension of the Technology Enhanced Interaction Framework (TEIF) and its “HI-Method” that had been developed and validated and evaluated for hearing impairment and supports other methods by providing multiple-choice questions to help identify requirements, the answers to which help provide technology suggestions that support the design stage. Four accessibility experts and three developer experts reviewed and validated the TEIF VI-Method. It was experimentally evaluated b...
บทคดยอ งานวจยนเปนงานวจยและพฒนามวตถประสงคเพอศกษา (1) วธการควบคมความถกตองของรายการหลกฐานหวเรอง (2) ... more บทคดยอ งานวจยนเปนงานวจยและพฒนามวตถประสงคเพอศกษา (1) วธการควบคมความถกตองของรายการหลกฐานหวเรอง (2) พฒนาโปรแกรมประยกตสาหรบควบคมรายการหลกฐานหวเรอง และ (3) วเคราะหและตรวจสอบรายการหลกฐานหวเรองของฐานขอมลสหบรรณานกรมสถาบนอดมศกษาไทย วธการศกษาประกอบดวย (1) การศกษาขอมลเบองตนจากเอกสารและการประชมรวมกบคณะกรรมการ ThaiLIS (2) การพฒนาโปรแกรมประยกตเพอควบคมรายการหลกฐานหวเรอง (3) การทดสอบโปรแกรม (System Testing) เพอวเคราะหและทดสอบรายการหลกฐานหวเรอง ผลการศกษาพบวา (1) วธการควบคมความถกตองควรมเงอนไขสาคญ 3 สวนคอ มรปแบบการทางานรวมกน มองคประกอบของระเบยน และมรปแบบการลงรายการระเบยนรายการหลกฐาน (2) คณสมบตของโปรแกรมประยกตมองคประกอบสาคญ 3 สวนคอ การสรางระเบยนอตโนมต การนาเขาระเบยน และการควบคมความถกตองของระเบยนรายการหลกฐาน (3) ผลการวเคราะหและตรวจสอบะเบยนรายการหลกฐาน พบวา สามารถวเคราะหและสรางระเบยนรายการหลกฐานหวเรองโดยอตโนมตไดจานวน 533,805 รายการ สามารถนาเขาระเบยนรายการหลกฐานหวเรองได 712,377 ระเบยน และสามารถตรวจสอบความซาซอนของรายการหวเรองไดจานวน 37,683 รายการ และเมอทดสอบโดยการปรบกฎเกณฑใหม พบวามระเบยนซาซอนลดลงรอยละ 10.1...
International Journal of Electrical and Computer Engineering, 2021
The benefit of exploratory testing and ad hoc testing by tester’s experience is that crucial bugs... more The benefit of exploratory testing and ad hoc testing by tester’s experience is that crucial bugs are found quickly. Regression testing and test case prioritization are important processes of software testing when software functions have been changed. We propose a test path prioritization method to generate a sequence of test paths that would match the testers’ interests and focuses on the target area of interest or on the changed area. We generate test paths form the activity diagrams and survey the test path prioritization from testers. We define node and edge weight to the symbols of activity diagrams by applying Time management, Pareto, Buffett, Binary, and Bipolar method. Then we propose a test path score equation to prioritize test paths. We also propose evaluation methods i.e., the difference and the similarity of test path prioritization to testers’ interests. Our proposed method had the least average of the difference and the most average of the similarity compare with the ...
2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018
Decision Tree classifiers are important classification techniques which are relatively faster lea... more Decision Tree classifiers are important classification techniques which are relatively faster learning speed and provide comparable classification accuracy with other methods. However, the categorical features must be handled before building decision tree model. There are various discretization techniques used to transfer continuous-value data into discrete-value data. It is difficult to select the appropriate discretization algorithms for different characteristics of data sets. In the experiments, we consider the class sample proportion to separate data sets with numerical attributes into two groups and apply with 12 commonly used discretization methods. We study both supervised versus unsupervised and top-down versus bottom-up approaches including ChiMerge, MDLP, Chi2, FUSINTER, Modified Chi2, CAIM, Extended Chi2, MODL, CACC, Ameva, PKID and ZDISC. The experimental results show that the MDLP, which uses class information entropy, is the best overall performance for Decision Tree, especially for different class sample proportion data sets. The CACC, which uses the contingency coefficient criterion, is the best algorithm for similar class sample proportion data sets. Both of them are supervised top-down approaches.
Informatica, 2021
Effort reduction in software testing is important to reduce the total cost of the software develo... more Effort reduction in software testing is important to reduce the total cost of the software development project. UML activity diagram is used by the tester for test path generation. It is hard to select the appropriate test path generation technique to diminish the effort of software testing. In the experiment, we compared the efficiency of 12 commonly-used test path generation techniques with both simple activity diagrams and the constructed complex activity diagrams. The experimental results summarized in four aspects. (1) The most appropriate test path generation technique for path testing generates the number of paths equivalent to the target number of all possible paths. (2) The suitable test path generation technique for the concurrency test scenario. (3) The techniques that can generate test paths covering basis path coverage in the case that testing all possible paths for the large or complex object-oriented method is laborious. (4) To compare the efficiency of test path generation algorithms, the percentage test path deviation to the target number of all possible paths is calculated for the constructed complex activity diagrams. We also recommended suitable test path generation methods for each manner of the UML activity diagram. Povzetek: Avtorji so analizirali uspešnost dvanajst metod za iskanje poti preverjanja programske opreme z enostavnimi in kompleksnimi diagrami aktivnosti.
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2003
The problem of extending binary support vector machines (SVMs) for multiclass classification is s... more The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the Adaptive Directed Acyclic Graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm-Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, Reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We then compare our performance with previous m...
The problem of extending binary support vector machines (SVMs) for multiclass classification is s... more The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. The Decision Directed Acyclic Graph (DDAG) method reduces training and evaluation time, while maintaining accuracy compared to the Max Wins, which is probably the currently most accurate method for multiclass SVMs. The Adaptive Directed Acyclic Graph (ADAG) approach is proposed to alleviate the problem of the DDAG structure. However, different sequences of binary classifiers in nodes in the ADAG may provide different accuracy. In this research we present a new method, Reordering Adaptive Directed Acyclic Graph (RADAG), which is the modification of the original ADAG method. We propose an algorithm to choose an optimal sequence of binary classifiers in nodes in the ADAG by considering the generalization error bounds of all classifiers. We apply minimum-weight perfect matching with the reordering algorithm in order to select binary classifiers which have small generalization errors to be used in data classification and in order to find the best sequence of binary classifiers in polynomial time. We then compare the performance of our method with previous methods including the DDAG, the ADAG and the Max Wins. Experiments denote that our method gives higher accuracy. Moreover it runs faster than Max Wins, especially when the number of classes and/or the number of dimensions are relatively large. In this research we also present alternative ways to enhance the performance of the RADAG and the DDAG as well. Department Computer Engineering Student's signature……………………………. Field of study Computer Engineering Advisor's signature……………………………. Academic year 2004 Co-advisor's signature………………………... vi ACKNOWLEDGEMENTS I am especially deeply grateful to my thesis advisor, Assistant Professor Dr. Boonserm Kijsirikul, who provided me with a great deal of guidance in the area of machine learning. He always pushes and motivates me throughout this research period. His valuable suggestions and comments made this work feasible. Moreover, he also gave me an opportunity getting the research fund − the Royal Golden Jubilee Ph.D. Program.
2014 International Joint Conference on Neural Networks (IJCNN), 2014
Multi-class classification is mandatory for real world problems and one of promising techniques f... more Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers. The minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion. Based on our method, each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers. Experimental results show that our technique gives significantly higher performance compared to traditional methods including the dense random code and the sparse random code both in terms of accuracy and classification times. Moreover, our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to the One-Versus-One.
Neurocomputing, Sep 11, 2013
We propose several novel methods for enhancing the multi-class SVMs by applying the generalizatio... more We propose several novel methods for enhancing the multi-class SVMs by applying the generalization performance of binary classifiers as the core idea. This concept will be applied on the existing algorithms, i.e., the Decision Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and Max Wins. Although in the previous approaches there have been many attempts to use some information such as the margin size and the number of support vectors as performance estimators for binary SVMs, they may not accurately reflect the actual performance of the binary SVMs. We show that the generalization ability evaluated via a cross-validation mechanism is more suitable to directly extract the actual performance of binary SVMs. Our methods are built around this performance measure, and each of them is crafted to overcome the weakness of the previous algorithm. The proposed methods include the Reordering Adaptive Directed Acyclic Graph (RADAG), Strong Elimination of the classifiers (SE), Weak Elimination of the classifiers (WE), and Voting based Candidate Filtering (VCF). Experimental results demonstrate that our methods give significantly higher accuracy than all of the traditional ones. Especially, WE provides significantly superior results compared to Max Wins which is recognized as the state of the art algorithm in terms of both accuracy and classification speed with two times faster in average.
ABSTRACT The problem of extending binary support vector machines (SVMs) for multiclass classifica... more ABSTRACT The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the adaptive directed acyclic graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm - Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We apply minimum-weight perfect matching with the reordering algorithm in order to select the best sequence of nodes in polynomial time. We then compare the performance of our method with previous methods including the ADAG and the Max Wins algorithm. Experiments denote that our method gives the higher accuracy, and runs faster than Max Wins.
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2003
Approaches for solving a multiclass classification problem by support vector machines (SVMs) are ... more Approaches for solving a multiclass classification problem by support vector machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some limitations in classification accuracy and evaluation time. This paper proposes a novel method that employs information-based dichotomization for constructing a binary classification tree. Each node of the tree is a binary SVM with the minimum entropy. Our method can reduce the number of binary SVMs used in the classification to the logarithm of the number of classes which is lower than previous methods. The experimental results show that the proposed method takes lower evaluation time while it maintains accuracy compared to other methods.
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Papers by Thimaporn Phetkaew