• 산업경영시스템학회지
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• 제44권2호
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• pp.15-23
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• 2021

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

• 한국통신학회논문지
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• 제30권1B호
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• pp.17-26
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• 2005

RFID(Radio Frequency IDentification) 기술은 RF 신호를 사용하여 물품에 부착된 전자태그를 비접촉식으로 식별하는 자동인식기술이다. RFID 시스템 구축에 있어 식별영역 내에 다수의 태그가 존재할 경우, 다중태그 식별(multi-tag identification)을 위한 충돌방지(anti-collision) 알고리즘이 필수적으로 요구된다. 태그 충돌방지와 관련된 기존 연구들은 각각 고유한 형태의 코드체계를 기반으로 하고 있으며 태그 식별성능에 대한 비교연구도 부족한 상태이다. 본 논문에서는 저비용(low-cost) RFID 시스템 구축을 목표로 표준화가 진행되고 있는 96-비트 EPC(Electronic Product Code) 코드를 기반으로 기존 대표적인 충돌방지 알고리즘인 트리 기반 메모리래스(tree based memoryless) 충돌방지 알고리즘들과 슬롯 알로하 기반으로 (slot aloha based) 충돌방지 알고리즘들의 성능평가를 수행한다. 성능평가 결과 초당 평균 태그 식별개수에서 충돌 추적 트리(collision tracking tree) 알고리즘이 다른 알고리즘들보다 최소 2배에서 최대 50배 이상의 우수한 성능을 보여준다.

• 한국통신학회논문지
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• 제32권4A호
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• pp.358-364
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• 2007

RFID(Radio Frequency IDentification) 기술은 라디오 주파수를 사용하는 비접촉 자동인식 기술이다. 이런 RFID 기술의 확산을 위해서는 리더(reader)가 다수의 태그(tag)를 짧은 시간 안에 인식하는 다중 태그 식별 문제를 해결 해야만 한다. 지금까지 이 문제를 해결하기 위한 충돌 방지(anti-collision) 알고리즘이 많이 개발되었고 이것들은 크게 알로하(ALOHA) 기반 알고리즘과 트리(tree) 기반 알고리즘으로 나뉜다. 본 논문에서는 이 두 가지 방법의 특징을 혼합한 새로운 충돌 방지 알고리즘 2가지를 제안한다. 그리고 대표적인 충돌 방지 알고리즘인 18000-6 Type A, Type B, Type C, query tree 알고리즘과 성능 비교 및 평가를 한다.

• 제어로봇시스템학회논문지
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• 제18권12호
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• pp.1096-1100
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• 2012

Most of previous genetic algorithms for solving graph problems have used a vertex-based encoding. We proposed an edge encoding based new genetic algorithm using a spanning tree. Contrary to general edge-based encoding, a spanning tree-based encoding represents only feasible partitions. As a target problem, we adopted the MAX CUT problem, which is well known as a representative NP-hard problem, and examined the performance of the proposed genetic algorithm. The experiments on benchmark graphs are executed and compared with vertex-based encoding. Performance improvements of the spanning tree-based encoding on sparse graphs was observed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권6호
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• pp.612-627
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• 2009

The high-level contribution of this paper is to illustrate the effectiveness of using graph theory tree traversal algorithms (pre-order, in-order and post-order traversals) to generate the chain of sensor nodes in the classical Power Efficient-Gathering in Sensor Information Systems (PEGASIS) data aggregation protocol for wireless sensor networks. We first construct an undirected minimum-weight spanning tree (ud-MST) on a complete sensor network graph, wherein the weight of each edge is the Euclidean distance between the constituent nodes of the edge. A Breadth-First-Search of the ud-MST, starting with the node located closest to the center of the network, is now conducted to iteratively construct a rooted directed minimum-weight spanning tree (rd-MST). The three tree traversal algorithms are then executed on the rd-MST and the node sequence resulting from each of the traversals is used as the chain of nodes for the PEGASIS protocol. Simulation studies on PEGASIS conducted for both TDMA and CDMA systems illustrate that using the chain of nodes generated from the tree traversal algorithms, the node lifetime can improve as large as by 19%-30% and at the same time, the energy loss per node can be 19%-35% lower than that obtained with the currently used distance-based greedy heuristic.

• 정보보호학회논문지
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• 제14권2호
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• pp.57-68
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• 2004

본 논문에서는 UOWHF의 도메인을 확장하기 위한 새로운 병렬 처리 알고리즘을 제안한다. 제시되는 알고리즘은 non-complete l-ary tree 에 기반을 두고 있으며 현재까지 최적의 키 길이를 가진 유일한 알고리즘인 Shoup 의 알고리즘과 동일한 최적의 키 길이를 가진다. 또한 Sarkar의 결과를 이용하여 본 논문에서 제시되는 알고리즘이 Shoup의 알고리즘과 함께 Sarkar가 제시한 도메인 확장 알고리즘들의 커다란 집합 중에서 가장 최적화된 키 길이를 가짐을 증명한다. 그러나 제안 알고리즘의 병렬처리능력은 complete tree에 기반 한 구성 방법들 보다 약간 비효율적이다. 그러나 만약 l이 점점 커진다면 알고리즘의 병렬처리능력도 complete tree 에 기반 한 방법들에 가까워진다.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• 산업공학
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• 제22권3호
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• pp.278-286
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• 2009

The anti-collision algorithms to identify a number of tags in real-time in RFID systems are divided into the anti-collision algorithms based on the Framed slotted ALOHA that randomly select multiple slots to identify the tags, and the anti-collision algorithms based on the Tree-based algorithm that repeat the questions and answer process to identify the tags. In the hybrid algorithm which is combined the advantages of these algorithms, tags are distributed over the frames by selecting one frame among them and then identified by using the Query tree frame by frame. In this hybrid algorithm, however, the time of identifying all tags may increase if many tags are concentrated in a few frames. In this study, to improve the performance of the hybrid algorithm, we suggest an improved algorithm that the tags select a specific group of frames based on the earlier bits of the tag ID so that the tags are distribute equally over the frames. By using the simulation and mathematical analysis, we show that the suggested algorithm outperforms traditional hybrid algorithm from the viewpoint of the number of queries per frame and the time of identifying all tags.

• 정보처리학회논문지D
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• 제14D권2호
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• pp.145-156
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• 2007

최근 웹 문서의 변조의 탐지, 버전 관리 등을 위한 XML/HTML 문서들에 대한 효과적인 실시간 변화탐지 알고리즘의 필요성이 증대하고 있다. 특히 대용량의 XML/HTML 문서들에 대한 실시간 변화탐지 응용들은 최소비용의 편집스크립트를 계산하는 알고리즘 보다는 실시간 처리가 가능한 빠른 휴리스틱 알고리즘들을 필요로 한다. 기존의 휴리스틱 알고리즘들은 실행속도는 빠르나 생성되는 편집스크립트의 질이 만족스럽지 못하다. 본 논문에서는 기존의 알고리즘 XyDiff와 X-tree Diff를 소개하고 이들 알고리즘들의 문제점들을 분석하고 문제점들을 개선한 알고리즘 X-tree Diff+를 제안한다. X-tree Diff+는 실행시간 측면에서 기존 알고리즘들과 유사하나 대응효율성에 기반한 대응과정의 개선을 통해 두 문서 간의 노트들의 대응률을 향상시킨 알고리즘이다.

• 응용통계연구
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• 제23권2호
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.