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Message from technical program chair
(2020-01-01)
Chamnongthai, Kosin
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Chinnasarn, Krisana
;
So-In, Chakchai
;
Horkeaw, Paramate
;
Phimoltares, Suphakant
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Message from technical program committee chairs
(2019-04-10)
Chamnongthai, Kosin
;
Chinnasarn, Krisana
;
Phimoltares, Suphakant
;
Jaiyen, Saichon
;
Rasmequan, Suwanna
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One-pass-throw-away learning for cybersecurity in streaming non-stationary environments by dynamic stratum network
(2018-09-01)
Thakong, Mongkhon
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Phimoltares, Suphakant
;
Jaiyen, Saichon
;
Lursinsap, Chidchanok
Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with the occurrence of cybersecurity issues, their methods repeatedly replicated the training processes for several times to classify datasets of these problems in streaming non-stationary environments. In dynamic environments, the conventional methods possibly deteriorate the adaptive solution to prevent these issues. This research proposes a one-pass-throw-away learning using the dynamical structure of the network to solve these problems in dynamic environments. Furthermore, to speed up the computational time and to maintain a minimum space complexity for streaming data, the new concepts of learning in forms of recursive functions were introduced. The information gain-based feature selection was also applied to reduce the learning time during the training process. The experimental results signified that the proposed algorithm outperformed the others in incremental-like and online ensemble learning algorithms in terms of classification accuracy, space complexity, and computational time.
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Fast Learning and Testing for Imbalanced Multi-Class Changes in Streaming Data by Dynamic Multi-Stratum Network
(2017-01-01)
Thakong, Mongkhon
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Phimoltares, Suphakant
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Jaiyen, Saichon
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Lursinsap, Chidchanok
Although several efficient learning methods have recently been proposed to handle class drift situations, issues remain in various streaming data applications that possibly deteriorate classification accuracy. Three important issues were considered, that is: 1) lifetime and class changes; 2) high imbalance ratios of streaming data among classes; and 3) classification accuracy of untrained data and class-changed data. A new dynamical learning structure based on hyper-elliptical capsule and multi-stratum network was introduced to cope with these issues. The experimental results on a simulated University of California at Irvine non-concept-drift database and real concept-drift data confirm that the proposed multi-stratum learning provided better accuracy, faster learning speed, and lower structural complexity than other concept-drift algorithms.
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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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