Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Janya Sainui
Prince of Songkla University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Unsupervised feature selection with least-squares quadratic mutual information Janya Sainui; Chouvanee Srivisal
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1619-1628

Abstract

We propose the feature selection method based on the dependency between features in an unsupervised manner. The underlying assumption is that the most important feature should provide high dependency between itself and the rest of the features. Therefore, the top m features with maximum dependency scores should be selected, but the redundant features should be ignored. To deal with this problem, the objective function that is applied to evaluate the dependency between features plays a crucial role. However, previous methods mainly used the mutual information (MI), where the MI estimator based on the k-nearest neighbor graph, resulting in its estimation dependent on the selection of parameter, k, without a systematic way to select it. This implies that the MI estimator tends to be less reliable. Here, we introduce the leastsquares quadratic mutual information (LSQMI) that is more sensible because its tuning parameters can be selected by cross-validation. We show through the experiments that the use of LSQMI performed better than that of MI. In addition, we compared the proposed method to the three counterpart methods using six UCI benchmark datasets. The results demonstrated that the proposed method is useful for selecting the informative features as well as discarding the redundant ones.
Learning color names using least-squares probabilistic classifiers Janya Sainui; Chouvanee Srivisal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp866-875

Abstract

Color name is one of the important features for computer vision. Many existing methods proposed to classify colors into a small number of color names. In this paper, we propose an alternative method with the goal to improve the accuracy for assigning a color name to an object in the given image. We here use the least-squares probabilistic classifiers (LSPC) with the local scaling parameters for solving this task. The benefit of the LSPC is that its solution can be computed analytically so that the obtained solution is global optimum, while the local scaling parameters play an important role to deal with the data including clusters with different local statistics as appeared in the real-world data. To deal with this task, the LSPC is learned to assign a color name to each pixel with the highest of the class-posterior density distribution. Then, the estimations of the class-posterior density distributions are utilized to compute the scores for predicting a color name to the given object. Lastly, the color name with the highest score is chosen as a predicted color name for that object. The experimental results on the eBay data set show the improvements over previously proposedmethods.
Co-Authors Chouvanee Srivisal