• The KIPS Transactions:PartB
• /
• v.12B no.4 s.100
• /
• pp.487-498
• /
• 2005

Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.6
• /
• pp.113-120
• /
• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.352-354
• /
• 2002

최근 들어, 대용량의 데이터를 처리할 수 있는 결정 트리 생성 방법에 많은 관심이 집중되고 있다. 그러나, 대용량 데이터를 위한 대부분의 알고리즘은 일괄처리 방식으로 데이터를 처리하기 때문에 새로운 예제가 추가되면 이 예제를 반영한 결정 트리를 생성하기 위해 처음부터 다시 재생성해야 한다. 이러한 재생성에 따른 비용문제에 보다 효율적인 접근 방법은 결정 트리를 순차적으로 생성하는 접근 방법이다. 대표적인 알고리즘으로 BOAT와 ITI를 들 수 있다. BOAT는 대용량 데이터를 지원하는 순차적 알고리즘이 지만 분할 포인트가 노드에서 유지하는 신뢰구간을 넘어서는 경우와 분할 변수가 변경되면 그에 영향을 받는 부분은 다시 생성해야 한다는 문제점을 안고 있고, 이에 반해 ITI는 분할 포인트 변경과 분할 변수 변경을 효율적으로 처리하지만 대용량 데이터를 처리하지 못해 오늘날의 순차적인 트리 생성 기법으로 적합하지 못하다. 본 논문은 ITI의 기본적인 트리 재구조화 알고리즘을 기반으로 하여 대용량 데이터를 처리하지 못하는 ITI의 한계점을 극복하기 위해 전역적 범주화 기법을 이용한 접근방법을 제안한다.

• The KIPS Transactions:PartB
• /
• v.13B no.3 s.106
• /
• pp.345-352
• /
• 2006

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.610-612
• /
• 2001

시간 제약 조건을 가진 타임드 페트리넷으로부터 최적화된 비동기식 제어회로를 생성하기 위해서는 시간 분석을 통하여 도달 가능한 상태만으로 구성된 축소 상태 그래프를 생성하는 작업이 매우 중요하다. 본 논문에서는 기존의 방법들이 적용 가능한 타임드 페트리넷의 범주에 제약을 가하거나 혹은 회로의 합성과는 직접적인 상관없이 시간 분석을 위하여 대규모의 시간 상태 그래프를 부가적으로 생성하는 문제를 해결하기 위하여 타임드 페트리넷으로부터 축소된 시간 상태 그래프를 직접적으로 생성하는 방법을 제안 한다 실험 결과는 제안된 방법이 모든 범주의 타임드 페트리넷으로부터 빠른 시간 내에 합성에 충분한 축소된 상태그래프를 생성함을 보여준다.

• The Journal of the Convergence on Culture Technology
• /
• v.5 no.4
• /
• pp.283-288
• /
• 2019

In order to overcome the students' lack of information and experience, we developed a content planning tree that utilizes a decision tree. The content planning tree consists of a tree trunk creation step in which students select a theme and a story to develop, a parent branch generation step for selecting a category that can be developed based on the story, a child branch generation step for selecting the interesting "effect" method of producing the content effectively, a leaf generation step for selecting a multimedia expression 'element' to be visualized. The educational model was applied to game planning design and information visualization lectures, and provides examples of the categories, effects, and elements used in each lecture. The model was used for 145 team projects and the efficiency was confirmed by a step-by-step learning process.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.949-959
• /
• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.1
• /
• pp.25-31
• /
• 2014

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules; these rules are formulated by language experts and consisted of lexical items. Such grammar checkers, unfortunately, show low recall which is detection ratio of errors in the document. In order to resolve this shortcoming, a new error-decision rule-generalization method that utilizes the existing KorLex thesaurus, the Korean version of Princeton WordNet, is proposed. The method extracts noun classes from KorLex and generalizes error-decision rules from them using the Tree Cut Model and information-theory-based MDL (minimum description length).

• Journal of KIISE
• /
• v.42 no.11
• /
• pp.1397-1403
• /
• 2015

A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.575-580
• /
• 2011

With the increasing expectation on personalized medicine, it is getting importance to analyze medical information in molecular biology perspective. Gene expression data are one of representative ones to show the microscopic phenomena of biological activities. In gene expression data analysis, one of major concerns is to identify markers which can be used to predict disease occurrence, progression or recurrence in the molecular level. Existing markers candidate identification methods mainly depend on statistical hypothesis test methods. This paper proposes a search method based decision tree induction to identify candidate markers which consist of multiple genes. The propose method discretizes numeric expression level into three categorical values and allows candidate markers' genes to be expressed by their negation as well as categorical values. It is desirable to have some number of genes to be included in markers. Hence the method is devised to try to find candidate markers with restricted number of genes.