• ETRI Journal
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• v.29 no.4
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• pp.527-529
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• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.514-522
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• 2003

Contrary to the previous methods, in this paper, we propose an enhanced shot change detection method using multiple features and binary decision tree. The previous methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using multiple features, which are supplementary each other, rather than using single feature. In order to classify the shot changes, we use binary classification tree. According to this classification result, we extract important features among the multiple features and obtain threshold value for each feature. We also perform the cross-validation and droop-case to verify the performance of our method. From an experimental result, it was revealed that the EI of our method performed average of 2% better than that of the conventional shot change detection methods.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.133-140
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• 2018

Accurate sentiment classification is an important research topic in sentiment analysis. This study suggests an efficient classification method of Korean sentiment using word2vec and ensemble methods which have been recently studied variously. For the 200,000 Korean movie review texts, we generate a POS-based BOW feature and a feature using word2vec, and integrated features of two feature representation. We used a single classifier of Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine and an ensemble classifier of Adaptive Boost, Bagging, Gradient Boosting, and Random Forest for sentiment classification. As a result of this study, the integrated feature representation composed of BOW feature including adjective and adverb and word2vec feature showed the highest sentiment classification accuracy. Empirical results show that SVM, a single classifier, has the highest performance but ensemble classifiers show similar or slightly lower performance than the single classifier.

• Journal of KIISE:Software and Applications
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• v.29 no.6
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• pp.386-395
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• 2002

In this paper, we suggest dynamic genetic tree-maps(DGTM) using optimal features on recognizing data. The DGTM uses the genetic algorithm about the importance of features rarely considerable on conventional neural networks and introduces GTM(genetic tree-maps) using tree structure according of the priority of features. Hence, we propose the extended formula, DGTM(dynamic GTM) has dynamic functions to separate and merge the neuron of neural network along the similarity of features.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.195-202
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• 2001

In this paper, we show a study on how to model a phoneme of which acoustic feature is changed according to both left-hand and right-hand phonemes. For this purpose, we make a comparative study on two kinds of algorithms; a unit reduction algorithm and decision tree modeling. The unit reduction algorithm uses only statistical information while the decision tree modeling uses statistical information and Korean acoustical information simultaneously. Especially, we focus on how to model context-dependent phonemes based on decision tree modeling. Finally, we show the recognition rate when context-dependent phonemes are obtained by the decision tree modeling.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.54-59
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.459-470
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• 2013

The discovery of nearest neighbors, without training in advance, has many applications, such as the formation of mosaic images, image matching, image retrieval and image stitching. When the quantity of data is huge and the number of dimensions is high, the efficient identification of a nearest neighbor (NN) is very important. This study proposes a variation of the KD-tree - the arbitrary KD-tree (KDA) - which is constructed without the need to evaluate variances. Multiple KDAs can be constructed efficiently and possess independent tree structures, when the amount of data is large. Upon testing, using extended synthetic databases and real-world SIFT data, this study concludes that the KDA method increases computational efficiency and produces satisfactory accuracy, when solving NN problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1514-1523
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• 2009

It is well known that the current wireless sensor networks addressing methods do not work efficiently in networks more than a few hundred nodes. A standard protocol in ZigBee-Standard feature in ZigBee 2007 gives balanced tree based address assignment method with distributed manner. However, it was limited to cover less than hundreds of sensor nodes due to the wasteful use of available address space, because composed sensor networks usually make an unbalanced tree topology in the real deployment. In this paper, we proposed the hierarchical cluster tree based address assignment method to support large and scalable networks. This method provides unique address for each node with distributed manner and supports hierarchical cluster tree on-demand. Simulation results show that the proposed method reduces orphan nodes due to the address exhaustion and supports larger network with limited address space compared with the ZigBee distributed address assignment method defined in ZigBee-Standard feature in ZigBee 2007.

• Korean Journal of Environment and Ecology
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• v.23 no.6
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• pp.535-544
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• 2009

This study was conducted to establish the selection criteria for the trees to be transplanted in the forest reserves which are expected to be developed in the future. The main task in this endeavor was to access the transplantability of the trees focused on their feature, diameter at breast height (D.B.H.), soil feature, etc. The selection of the trees for transplantation consisted of two stages. The first stage was to select trees on the basis of their indigenousness and forest successional stage. The second was to select trees on the basis of their type, D.B.H., the layers of soil, etc. At the first stage, the trees which are not indigenous or expected not to survive were eliminated from the selection list, and the result showed that approximately 5.9% (about 3,841 trees) of the trees proved to be inadequate for transplanting. At the second stage, the investigation of the trees based on the criteria of tree type, D.B.H., the layers of soil was carried out, and the result showed that approximately 33.7% (1,218) out of 3,613 trees turned out to adequate for transplanting however, 23.0% of the trees, which are 829 trees, were found to be impossible to transplant. In addition, it was discovered that in the case of approximately 43.3%(1,566 trees) of the trees there was little difference between transplanting cost and planting cost of new trees. Therefore the investigation indicated that it is more advisable to transplant trees to preserve the ecological environment. However, the study showed that there are other elements to be considered, such as tree feature and soil condition, for the successful tree transplantation, and the necessary information can be provided by the managing personnel who are in charge of the forest.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.33-47
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• 2007

The requirements have been changed during development progresses, since it is impossible to define all of software requirements. These requirements change leads to mistakes because the developers cannot completely understand the software's structure and behavior, or they cannot discover all parts affected by a change. Requirement changes have to be managed and assessed to ensure that they are feasible, make economic sense and contribute to the business needs of the customer organization. We propose a feature-oriented requirements change management method to manage requirements change with value analysis and feature-oriented traceability links including intermediate catalysis using features. Our approach offers two contributions to the study of requirements change: (1) We define requirements change tree to make user requirements change request generalize by feature level. (2) We provide overall process such as change request normalization, change impact analysis, solution dealing with change request, change request implementation, change request evaluation. In addition, we especially present the results of a case study which is carried out in asset management portal system in details.