• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.519-529
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• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.313-316
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• 2011

The skyline of a multi-dimensional data set is a subset that consists of the data that are not dominated by other members of the set. Skyline computation can be very useful for decision making for multi-dimensional data set. However, in case that the skyline is very large, it may not be much useful for decision making. In this paper, we propose an algorithm for computing a part of the skyline considering location restrictions that the user provides, such as origin movement, degree ranges and/or distances from the origin. The algorithm eliminates noncandidate data rapidly, and returns in order the skyline points that satisfy the user's requests. We show that the algorithm is efficient by experiments.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.865-870
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• 2006

HCI 연구의 주된 주제는 인간중심의 상호작용 환경의 개발이다. 이러한 개발과정에서 새로운 인터페이스 환경이 실생활에 어떠한 기능적 효용가치를 가져다 줄 것인가에 관한 문제가 중요하게 고려되어야 한다. 이를 위해 실질적인 적용사례 구축을 통해 효용성을 입증하는 것이 필요하다. 또한, 최근의 HCI는 기존의 인터페이스 수단을 대체하는 것을 목적으로 개발되어 왔으나, 보다 발전된 접근방법으로서 기존의 인터페이스가 소화할 수 없었던 상호작용의 의미론적 요소들을 다루는 것이 필요할 것이다. 이러한 맥락에서 기존 컴퓨팅 환경에서의 문제해결 프로세스의 한 예를 고찰하고 이것이 새로운 HCI환경에서 효과적인 방법으로 어떻게 구현될 수 있는가에 대한 구체적인 사례를 연구하였다. 본 논문은 문제해결의 한 예로서, '복수개의 결정사안 중 최선의 방안을 도출하는 의사결정과정'에서 HCI를 접목한 효과적인 의사결정 프로세스를 제안하고 이의 효용성을 검증한다. 이러한 의사결정 방법론으로 기존에 사용되는 AHP(Analytic Hierarchy Process)가 대표적이다. 일반적으로 AHP는 각 고려인자간 쌍대비교(pairwise comparisons)를 통해 중요도를 평가하는 과정을 포함한다. 이 과정을 통해 각각의 인자간의 쌍대비교치를 결정한 후 일련의 계산과정을 거쳐 그 결과를 도출한다. 이 작업은 통상적인 데스크탑 컴퓨터 환경에서 이루어진다. 본 논문에서는 각 인자간의 쌍대비교를 통한 우선순위를 결정하는 과정에서 새로운 인터페이스 환경의 적용을 위한 효과적인 연산 알고리즘을 제안하고 이의 효용성을 검증한다. 또한 의사결정 과정의 직관적 가시화를 위해 기본적인 프리미티므 도형으로 이루어진 그래픽 인터페이스를 구현하고, 기존의 의사결정 과정과 그 효용성을 비교한다.

• Science of Emotion and Sensibility
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• v.10 no.1
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• pp.23-36
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• 2007

This research focused on eliciting and analyzing mental models of mobile device purchasing consumers who are distinguished by their familiarity with information technology. Mental model elicitation processes proceeded by critical decision method. And Pathfinder algorithm and Social Network Analysis were used to analyze the mental models. The results show that IT-familiar consumers have mental models of which elements are more organized and distinctive while IT-unfamiliar consumers have vague and socially affected mental models.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.97-102
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• 2003

데이터 마이닝의 여러 기법중 모형의 변동성을 줄이고 정확도가 높은 분류자를 형성하기 위하여 다양한 앙상블 기법이 연구되고 있다. 그 중에서 배깅과 부스팅 방법이 가장 널리 알려져 있다. 여러 가지 데이터에 이 두 방법을 적용하여 오분류율을 구하여 비교한 후 각 데이터 특성을 입력변수로 하고 배깅과 부스팅 중 더 낮은 오분류율을 갖는 알고리즘을 목표변수로 하여 의사결정나무를 형성하였다. 이를 통해서 배깅과 부스팅 알고리즘이 어떠한 데이터 특성의 패턴이 존재하는지 분석한 결과 부스팅 알고리즘은 관측치, 입력변수, 목표변수 수가 큰 것이 적합하고 반면에 배깅 알고리즘은 관측치, 입력변수, 목표변수 수의크기가 작은 것이 적합함을 알 수 있었다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.978-982
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• 2009

This paper proposes a potential-based reinforcement learning, called "RLs-CBDT", which combines multiple RL agents and case-base decision theory designed for decision making in uncertain environment as an expert knowledge in RL. We empirically show that RLs-CBDT converges to an optimal policy faster than pre-existing RL algorithms through a Tetris experiment.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.149-161
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• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.505-515
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• 2017

This study examines the verification of algorithm on resilience leisure programs for the productive aging of the new elderly in Korea. The subjects for this study were 525 new elderly who lived in metropolis, medium-sized cities and farming area. The reliability and validity test of the questionnaire were conducted by using SPSS 20.0 program; the results of tree analysis are as follows; First, The influential factor in the resilience leisure programs is subjective health status, desire for activity, interpersonal exchange and household income. The most influential resilience factor of algorithm is interpersonal relationship, self-regulating and affirmative. The structural algorithm of resilience was that low interpersonal relationship group related to the affirmative and high interpersonal relationship group related to the self-regulating.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.229-237
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• 2010

Recently, forest management objectives become more complex and complicated, and spatial constraints were necessarily considered for ecological stability. Now forest planning is required to provide an optimized solution that is able to achieve a number of management objectives and constraints. In this study, we developed a decision supporting system based on the one of dynamic planning techniques, Tabu Search (TS) heuristic algorithm, which enable one to generate an optimized solution for given objectives and constraints. For this purpose, we analyzed the logical flow of the algorithm and designed the subsequence of processes. To develop a high-performance computing system, we examined a number of strategy to minimize execution time and workloads in each process and to maximize efficiency of using system resources. We examined two model based on the original TS algorithm and revised version of TS algorithm and compared their performance in optimization process. The results showed high performance of the developed system in providing feasible solutions for several management objectives and constraints. Moreover, the revised version of TS algorithm was appeared to be more stable for providing results with minimum variation. The developed system is expected to use for developing forest management plans in Korea.