• Journal of Korean Institute of Industrial Engineers
• /
• 제27권4호
• /
• pp.394-405
• /
• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• Journal of Information Processing Systems
• /
• 제18권4호
• /
• pp.500-509
• /
• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• The KIPS Transactions:PartB
• /
• 제12B권4호
• /
• 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.

• Proceedings of the Korean Information Science Society Conference
• /
• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (2)
• /
• pp.60-62
• /
• 2000

인터넷의 활성화로 많은 사람들이 인터넷을 이용하고 이에 따라 인터넷을 이용한 서비스도 홍수를 이루고 있다. 이에 따라 인터넷을 상업적 목적으로 사용하는 서비스도 증가하고 있다. 그러나 많은 인터넷 서비스들이 고객들에게 획일적이고 일률적인 서비스만을 제공한다. 각각의 고객에게 취향과 관심분야에 따른 차별화 된 서비스가 필요로 한다. 각 고객에게 1대 1로 차별화 된 service를 제공하기 위해서 먼저 각 고객을 구별하고 그 고객의 취향과 관심분야의 파악을 위해서 인터넷에서의 행동을 관찰한다. 또한 고객의 관리를 위해 고객을 필요에 따라 그룹화하고, 고객과 직접 접촉을 통해 고객 정보를 파악할 수도 있다. 파악된 고객 정보의 효율적 저장과 분석을 위해서 decision tree를 이용해 학습을 한다. 고객의 행동의 특성상 incremental한 학습 알고리즘을 사용하며 고객의 선호도를 이용한 decision tree를 이용한다. 학습된 결과를 이용해서 1대 1 서비스를 제공함으로써 고객에서 편리성을 제공하고 서비스에 대한 친밀감과 고객의 흥미를 유발할 수 있다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.419-419
• /
• 1996

• Journal of the Korean Operations Research and Management Science Society
• /
• 제27권2호
• /
• pp.33-49
• /
• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• Journal of information and communication convergence engineering
• /
• 제11권4호
• /
• pp.258-267
• /
• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권4호
• /
• pp.1442-1462
• /
• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• 제14권9호
• /
• pp.1979-1986
• /
• 2010

Biological early warning system detects toxicity by looking at behavior of organisms in water. The system uses classifier for judgement about existence and amount of toxicity in water. Boosting algorithm is one of possible application method for improving performance in a classifier. Boosting repetitively change training example set by focusing on difficult examples in basic classifier. As a result, prediction performance is improved for the events which are difficult to classify, but the information contained in the events which can be easily classified are discarded. In this paper, an incremental learning method to overcome this shortcoming is proposed by using the extended data expression. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression by exploiting the necessary information not only from the well classified, but also from the weakly classified events. Experimental results show that the new algorithm outperforms the former Learn++ method without using the weight parameter.

• Journal of Hospice and Palliative Care
• /
• 제25권2호
• /
• pp.76-84
• /
• 2022

Purpose: We compared cost-effectiveness parameters between inpatient and home-based hospice-palliative care services for terminal cancer patients in Korea. Methods: A decision-analytic Markov model was used to compare the cost-effectiveness of hospice-palliative care in an inpatient unit (inpatient-start group) and at home (home-start group). The model adopted a healthcare system perspective, with a 9-week horizon and a 1-week cycle length. The transition probabilities were calculated based on the reports from the Korean National Cancer Center in 2017 and Health Insurance Review & Assessment Service in 2020. Quality of life (QOL) was converted to the quality-adjusted life week (QALW). Modeling and cost-effectiveness analysis were performed with TreeAge software. The weekly medical cost was estimated to be 2,481,479 Korean won (KRW) for inpatient hospice-palliative care and 225,688 KRW for home-based hospice-palliative care. One-way sensitivity analysis was used to assess the impact of different scenarios and assumptions on the model results. Results: Compared with the inpatient-start group, the incremental cost of the home-start group was 697,657 KRW, and the incremental effectiveness based on QOL was 0.88 QALW. The incremental cost-effectiveness ratio (ICER) of the home-start group was 796,476 KRW/QALW. Based on one-way sensitivity analyses, the ICER was predicted to increase to 1,626,988 KRW/QALW if the weekly cost of home-based hospice doubled, but it was estimated to decrease to -2,898,361 KRW/QALW if death rates at home doubled. Conclusion: Home-based hospice-palliative care may be more cost-effective than inpatient hospice-palliative care. Home-based hospice appears to be affordable even if the associated medical expenditures double.