• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1259-1265
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• 2004

In many applications, producing labeled data is costly and time consuming while an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification teaming. In machine learning literature, the co-training method has been widely used for this purpose. However, the current co-training method requires the entire features to be split into two independent sets. Therefore, in this paper, we improved the current co-training method in a number of ways, and proposed a new co-training method which do not need the feature split. Experimental results show that our proposed method can significantly improve the performance of the current co-training algorithm.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.49-57
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• 1994

진정한 의미의 다운사이징을 구현하기 위해서는 필요한 분산데이타베이스의 구현은 현재 여러가지 기술적인 문제점들을 안고 있다. 따라서 동시성 제어(concurrency control)와 갱신 (update propagation), 복구(recovery), 질의어 처리(query processing), 카달로그 관리(catalog management)등과 같은 분산 환경에서의 데이타베이스에 관련된 기술적인 문제점들을 해결하기 위해서는 어떠한 최적의 방법들을 개발해야만 한다. 이러한 방법들의 개발은 관계형 데이타베이스의 데이타관리 분류체계를 통한 대안들의 선택과 운용에 의해 가능할 것이다. 분산 환경하에서 사용되어질 수 있는 관계형 데이타베이스의 데이타관리 분류체계를 availability, expression, currency의 관점에서 Basic table과 view로 구분하여 정립해 보았다. Basic table은 current update가 필수적이므로 availability와 expression의 관점에서 분류하였고, view는 physical file의 존재성 유무와 시간적 실행의 차이를 기준으로 분류하였다. 그리고 이러한 분류기준에 따른 특성들을 분산데이타베이스 구축에 이용하는 방법들에 대해 이야기 하였다. 다시 말해 non-current materialized view 뿐만 아니라 current materialized view를 동시에 지원하는 하나의 distributed view update architecture를 개발하는 것에 대해 하나의 방법으로 제시하였다. 즉 immediate update와 deferred update는 current view를 이용하고 periodical update는 non-current view를 이용하여 100%의 distributed data resources를 관리 할 수 있는 효율적인 distributed system를 개발하는 것을 제시하였다. 본 논문은 데이타베이스론의 입장에서 아직 정립되어 있지 않은 분산 환경하에서의 관계형 데이타베이스의 데이타관리의 분류체계를 나름대로 정립하였다는데 그 의의가 있다. 또한 이것의 응용은 현재 분산데이타베이스 구축에 있어 나타나는 기술적인 문제점들을 어느정도 보완할 수 있다는 점에서 그 중요성이 있다.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.14-21
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• 1995

This paper presents a parallel sorting algorithm suitable for the SIMD multiprocessor. The algorithm finds pivots for partitioning the data into ordered subsets. The data can be evenly distributed to be sorted since it uses the probability theory. For n data elements to be sorted on p processors, when $n{\geq}p^2$, the algorithm is shown to be asymptotically optimal. In practice, sorting 8 million data items on 64 processors achieved a 48.43-fold speedup, while the PSRS required a 44.4-fold speedup. On a variety of shared and distributed memory machines, the algorithm achieved better than half-linear speedups.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.991-998
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• 2004

In many practical teaming problems including bioinformatics area, there is a small amount of labeled data along with a large pool of unlabeled data. Labeled examples are fairly expensive to obtain because they require human efforts. In contrast, unlabeled examples can be inexpensively gathered without an expert. A common method with unlabeled data for data classification and analysis is co-training. This method uses a small set of labeled examples to learn a classifier in two views. Then each classifier is applied to all unlabeled examples, and co-training detects the examples on which each classifier makes the most confident predictions. After some iterations, new classifiers are learned in training data and the number of labeled examples is increased. In this paper, we propose a new co-training strategy using unlabeled data. And we evaluate our method with two classifiers and two experimental data: WebKB and BIND XML data. Our experimentation shows that the proposed co-training technique effectively improves the classification accuracy when the number of labeled examples are very small.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.1045-1050
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• 1998

Classification techniques are often an importand component of intelligent systems and are use for both deta preprocessing and decision making. In the design of a classification system, the labled samples must be given to provide a priori information for the classification. Moreover, the number of classes to be categorized must be known a priori information, called OFCAM. In OFCAM, an unsupervised by OFCAM, the database of a classification system, called PCSDB, is constructed. Then, PCSDB can be effectively used in the decision process of the system.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.46-58
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• 2007

Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarry experiment. In order to increase the classification accuracy, it is desirable to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a novel two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy up to 24.19% increase as against other comparison methods, sensitivity, and specificity on independent test sample dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.2
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• pp.85-92
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• 1989

The aim of this study is to find out the combination of effective transformed data, applying Remote Sensing techniques, as to the classification and particular objects by transforming the MSS data and TM data of the satellite LANDSAT into several linearly transformed data. Since one of the problems in the processing of the LANDSAT data is the vastness of the data, the Linear Transformation could be a method to perform analysis of those vast data, more efficiently and economically. This method is carried out as follows : (1) offering the simplicity over complex data, (2) selectional processing over redundant data and removing unnecessary data, (3) emphasizing on the object of the study ; by transforming multispectral data through linear calculation and statistical transformation. In this study, the analysis and transformation of the data have been performed by means of Band Ratioing and Principal Component Analysis. As the classificatory consequence, Infrared/RED Ratioing which expands the characterization of green vegetation, has been useful for a distinctive classification among other classes. For the Principal Component Analysis, band 1,2,7 are efficient in the classification of the green vegetation.