• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.391-396
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• 2003

This paper proposes a new approach that converts continuous-valued attributes to categorical-valued ones considering the distribution of target attributes(classes). In this approach, It can be possible to get optimal interval boundaries by considering the distribution of data itself without any requirements of parameters. For each attributes, the distribution of target attributes is projected to one-dimensional space. And this space is clustered according to the criteria like as the density value of each target attributes and the amount of overlapped areas among each density values of target attributes. Clusters which are made in this ways are based on the probabilities that can predict a target attribute of instances. Therefore it has an interval boundaries that minimize a loss of information of original data. An improved performance of proposed discretization method can be validated using C4.5 algorithm and UCI Machine Learning Data Repository data sets.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.65-74
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• 2009

In optimizing the neural network structure, there are two methods of the pruning scheme and the constructive scheme. In this paper we use the pruning scheme to optimize neural network structure, and the genetic algorithm to find out its optimum node pruning. In the conventional researches, the input and hidden layers were optimized separately. On the contrary we attempted to optimize the two layers simultaneously by encoding two layers in a chromosome. The offspring networks inherit the weights from the parent. For teaming, we used the existing error back-propagation algorithm. In our experiment with various databases from UCI Machine Learning Repository, we could get the optimal performance when the network size was reduced by about $8{\sim}25%$. As a result of t-test the proposed method was shown better performance, compared with other pruning and construction methods through the cross-validation.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.151-155
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• 2019

Recently heart attack is 80% of the sudden death of elderly. The causes of a heart attack are complex and sudden, and it is difficult to predict the onset even if prevention or medical examination is performed. Therefore, early diagnosis and proper treatment are the most important. In this paper, we show the accuracy of normal and abnormal classification with neural network using weighted fuzzy function for accurate and rapid diagnosis of myocardial infarction. The data used in the experiment was data from the UCI Machine Learning Repository, which consists of 14 features and 303 sample data. The algorithm for feature selection uses the average of weight method. Two features were selected and removed. Heart attack was classified into normal and abnormal(1-normal, 2-abnormal) using the average of weight method. The test result for the diagnosis of heart attack using a weighted fuzzy neural network showed 87.66% accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.125-130
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• 2016

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.267-270
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• 2007

기계학습에서 분류기들의 집합으로 구성된 앙상블 분류기는 단일 분류기에 비해 정확도가 높다는 것이 입증되었다. 본 논문에서는 새로운 앙상블 학습으로서 데이터의 지역 기반 분류기들의 앙상블 학습을 제시하여 기존의 앙상블 학습과의 비교를 통해 성능을 검증하고자 한다. 지역 기반 분류기의 앙상블 학습은 데이터의 분포가 지역에 따라 다르다는 점에 착안하여 학습 데이터를 분할하고 해당하는 지역에 기반을 둔 분류기들을 만들어 나간다. 이렇게 만들어진 분류기들로부터 지역에 따라 가중치를 둔 투표를 하여 앙상블 방법을 이끌어낸다. 본 논문에서 제시한 앙상블 분류기의 성능평가를 위해 UCI Machine Learning Repository에 있는 11개의 데이터 셋을 이용하여 단일 분류기와 기존의 앙상블 분류기인 배깅과 부스팅등의 정확도를 비교하였다. 그 결과 기본 분류기로 나이브 베이즈와 SVM을 사용했을 때 새로운 앙상블 방법이 다른 방법보다 좋은 성능을 보이는 것을 알 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.335-337
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• 2024

This paper presents a new open-world object detection method emphasizing uncertainty representation in machine learning models. The focus is on adapting to real-world uncertainties, incrementally updating the model's knowledge repository for dynamic scenarios. Applications like autonomous vehicles benefit from improved multi-class classification accuracy. The paper reviews challenges in existing methodologies, stressing the need for universal detectors capable of handling unknown classes. Future directions propose collaboration, integration of language models, to improve the adaptability and applicability of open-world object detection.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).