• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1424-1434
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• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.120-128
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• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.983-987
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• 2009

One of the critical themes in the language acquisition is its exposure to linguistic environments. Linguistic environments, which interact with infants, include not only human beings such as its parents but also artificially crafted linguistic media as their functioning elements. An infant learns a language by exploring these extensive language environments around it. Based on such large linguistic data exposure, we propose a machine learning based method on the cognitive mechanism that simulate flexibly and appropriately infant's language learning. The infant's initial stage of language learning comes with sentence learning and creation, which can be simulated by exposing it to a language corpus. The core of the simulation is a memory-based learning model which has language hypernetwork structure. The language hypernetwork simulates developmental and progressive language learning using the structure of new data stream through making it representing of high level connection between language components possible. In this paper, we simulates an infant's gradual and developmental learning progress by training language hypernetwork gradually using 32,744 sentences extracted from video scripts of commercial animation movies for children.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.393-395
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• 2012

많은 현대인들은 체중 관리를 위해 많은 시간과 노력을 쏟고 있으며 그중에서도 식단을 관리하는데 많은 힘을 기울이고 있다. 하지만, 전문지식이 없는 일반인이 자신이 먹은 식단을 분석하고 어떤 음식을 먹을지 계획하는 것은 쉽지 않다. 따라서 본고에서는 hypernetwork를 이용한 개인 맞춤형 식단 추천 알고리즘을 제안한다. 개발된 식단 추천 알고리즘은 사용자의 식단 로그 데이터를 기반으로 사용자의 식성에 맞고 적절한 칼로리를 지닌 식단을 구성하여 추천한다. 특히, 식품 정보 DB 이외에 다른 추가 정보가 필요하지 않으며, 개인의 작은 식단 로그 데이터만으로도 동작 가능한 장점을 가지고 있다. 본 연구실에서는 개발된 알고리즘을 이용하여 개인 체중 관리 어플리케이션인 DietAdvisor를 제작하였으며, 사용자는 어플리케이션을 통해 실제 식단 추천 및 그 외의 체중관리에 필요한 서비스를 제공받을 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.352-354
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• 2006

패턴완성(Pattern Completion)은 사용되는 패턴 성분들 사이의 higher-order correlation 정보가 중요한 의미를 가질 수 있는 기계학습 문제 중 하나이다. higher-order correlation은 확률라이브러리모델(Probabilistic Library Model)로 구현되는 hypernetwork 개념을 도입해서 나타낼 수 있다. 하지만 확률라이브러리모델을 사용하여 higher-order 정보를 나타내려할 때 초기라이브러리가 모든 가능한 조합의 원소들을 가지도록 구성하기는 쉽지 않다. 그 대안으로 초기라이브러리 구성 시 학습패턴들을 subsampling하여 적은 숫자의 원소들만으로 higher-order correlation의 근사치를 나타내게 할 수 있다. 본 논문에서는 이와 같이 subsampling이 사용되어 구성된 확률라이브러리모델을 이용한 패턴완성시의 correlation의 order에 따른 효과를 분석하여 본다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.289-294
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• 2008

어떤 자연언어 문서가 전달하려는 의미는 그 텍스트의 성격에 따라 아주 명확할 수도(예: 뉴스 문서), 아주 불분명할 수도 있다(예: 시). 이 연구는 이러한 '의미의 명확성(semantic transparency)'을 정량적으로 측정할 수 있다고 가정하고, 이 의미의 명확성을 판단하는 데에 단어들의 연쇄(word association)의 확률통계적 성질들이 어떻게 기능하는지에 대해 논한다. 이를 위해 특정 단어가 연쇄체를 형성하면서 발생하는 neighboring frequency와 degeneracy를 중심으로 Markov chain Monte Carlo scheme을 적용하여 의미망('Semantic Hypernetwork')으로 학습시킨 후 문서의 구성 단어들과 그 집합들 간의 연결 상태를 파악하였다. 우리는 의미적으로 그 표상이 분명하게 나뉘는 문서들(뉴스와 시)을 대상으로 이 모델이 어떻게 이들의 의미적 명확성을 분류하는지 분석하였다. Neighboring frequency와 degeneracy, 이 두 속성이 언어구조에서의 의미망 기억과 학습 탐색 기제에 유의한 기질로서 제안될 수 있다. 본 연구의 주요 결과로 1) 텍스트의 의미론적 투명성을 구별하는 통계적 증거와, 2) 문서의 의미구조에 대한 새로운 기질 발견, 3) 기존의 문서의 카테고리 별 분류와는 다른 방식의 분류 방식 제안을 들 수 있다.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.205-213
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• 2010

We introduce a natural language sentence generation (NLG) method based on learning of word-association patterns. Existing NLG methods assume the inherent grammar rules or use template based method. Contrary to the existing NLG methods, the presented method learns the words-association patterns using only the co-occurrence of words without additional information such as tagging. We employ the hypernetwork method to analyze and represent the words-association patterns. As training going on, the model complexity is increased. After completing each training phase, natural language sentences are generated using the learned hyperedges. The number of grammatically plausible sentences increases after each training phase. We confirm that the proposed method has a potential for learning grammatical properties of training corpuses by comparing the diversity of grammatical rules of training corpuses and the generated sentences.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.122-123
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• 2008