• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.9-16
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• 2020

Pronunciation recognition systems that use only video information and ignore voice information can be applied to various customized services. In this paper, we develop a system that applies a Bayesian classifier to distinguish Korean vowels via lip shapes in images. We extract feature vectors from the lip shapes of facial images and apply them to the designed machine learning model. Our experiments show that the system's recognition rate is 94% for the pronunciation of 'A', and the system's average recognition rate is approximately 84%, which is higher than that of the CNN tested for comparison. Our results show that our Bayesian classification method with feature values from lip region landmarks is efficient on a small training set. Therefore, it can be used for application development on limited hardware such as mobile devices.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.485-490
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• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.945-952
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• 2007

The face detection is a necessary first-step in the face recognition systems, with the purpose of localizing and extracting face regions from input images. But it is not a simple problem, because faces have many variations such as scale, rotation and lighting condition. In this paper, we propose a novel method to detect not only frontal faces but also partial rotated faces in still images. Firstly, we produce the eye candidates in the sub-regions of an input image to detect rotated faces. Secondly, the eye candidates are used to measure the angles of rotated faces. Thirdly, we are able to derotate the rotated face then put it to Bayesian classifier. We make an experiment with rotated multiracial face and show the good results in this paper.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.19-26
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• 2019

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1052-1061
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• 2006

This paper presents a method of using virtual examples to improve the classification accuracy for data with nominal attributes. Most of the previous researches on virtual examples focused on data with numeric attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naive Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the network's conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way.can help various learning algorithms to derive classifiers of improved accuracy.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1065-1074
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• 2007

Among various methods to compare reference signatures with an input signature, the segment-to-segment matching method has more advantages than global and point-to-point methods. However, the segment-to-segment matching method has the problem of having lower recognition rate according to the variation of partitioning points. To resolve this drawback, this paper proposes a signature verification method by considering linear discriminant analysis as well as segment-to-segment matching method. For the final decision step, we adopt statistical based Bayesian classifier technique to effectively combine two individual systems. Under the various experiments, the proposed method shows better performance than segment-to-segment based matching method.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.533-540
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• 2014

We propose an age estimation-based smart digital signage for the next-generation airport service. The proposed system can recognize the face of the customer so that it can display the selective information. Using a webcam, the system captures the face of the customer and estimates the age of the customer by calculating the wrinkle density of the face and applying bayesian classifier. The developed age estimation method is tested with a face database for the performance evaluation. We expect the new digital signage may improve the satisfaction of customers of the airport business.

• Annual Conference of KIPS
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• 2017.11a
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• pp.742-745
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• 2017

본 논문은 심장질환 도메인에 데이터 마이닝 기법을 적용한 연구로, 기존 환자의 정보에 대하여 K-최근접 이웃 알고리즘을 통해 결측 값을 대체하고, 대표적인 예측 분류기인 나이브 베이지안, 소포트 벡터 머신, 그리고 다층 퍼셉트론을 적용하여 각각 결과를 비교 및 분석한다. 본 연구의 실험은 K 최적화 과정을 포함하고 10-겹 교차 검증 방식으로 수행되었으며, 비교 및 분석은 정확도와 카파 통계치를 통해 판별한다.