• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.58-64
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• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.73-78
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• 2003

This paper proposes the simulator synthesis scheme which is based on the exploration of the total design space in attributed AND-OR graph. Attributed AND-OR graph is a systematic design space representation formalism which enables to represent all the design space by decomposition rule and specialization rule. In addition, attributes attached to the design entity provides flexible modeling. Based on this design space representation scheme, a pruning algorithm which can transform the total design space into sub-design space that satisfies the user requirements is given. We have shown the effectiveness of our framework by (ⅰ) constructing the design space of superscalar processor in attributed AND-OR graph (ⅱ) pruning it to obtain the ARM9 processor architecture. (ⅲ) modeling the components of the architecture and (ⅳ) simulating the ARM9 model.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.725-728
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• 2011

멀티홉 네트워크에서 플러딩(Flooding) 기법은 토폴로지 내의 모든 노드에게 패킷을 전달하는 것이다. 대표적인 플러딩 기법인 Blind 플러딩은 패킷을 받은 모든 노드가 플러딩을 하기 때문에, 무선 네트워크의 전체적인 성능이 감소한다. 기존 연구에서는 성능 향상을 위해 중복 수신되는 패킷을 줄이는 데에만 초점이 맞춰져 있다. 하지만 실제 무선 네트워크 환경에서는 간섭에 따른 패킷 손실이 발생하고, 플러딩은 Broadcast 로 전송하기 때문에 재전송하여 손실 패킷을 복구할 수 없다. 본 논문에서는 Blind, Self-Pruning, Dominant-pruning 플러딩 기법에 재전송이 필요 없는 오류정정 기법(FEC)를 적용하여, 추가적인 잉여 데이터에 따른 전체 전송 패킷의 수와 플러딩 기법의 신뢰성을 분석 하였다.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.498-503
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• 2017

SIMD (Single Instruction Multiple Data) is a representative parallelization architecture that processes multiple data loaded in a SIMD register with a single instruction. Quicksort is a sorting algorithm that picks an element as a pivot from the array and reorders the array such that all elements having the values less than the pivot value are located in the left side on the pivot as well as all elements having the value greater than the pivot value are located in the right side on the pivot and then the algorithm performs the same task on both sublist recursively. In this paper, we propose an efficient Quicksort algorithm applying the SIMD instructions which minimally invokes conditional branches to avoid the performance degradation incurred by branch misprediction in a pipeline architecture. In addition, we improve the performance of the Quicksort algorithm by fetching data into a SIMD register as a byte unit to apply VBP (Vertical Bit Parallel) and the early pruning technique.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.121-133
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• 1998

This study suggests an efficient algorithm for updating discovered association rules in large database, because a database may allow frequent or occasional updates, and such updates may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. FUP and DMI update efficiently strong association rules in the whole updated database reusing the information of the old large item-sets. Moreover, these algorithms use a pruning technique for reducing the database size in the update process. This study updates strong association rules efficiently in the whole updated database reusing the information of the old large item-sets. An updating algorithm that is suggested in this study generates the whole candidate item-sets at once in an incremental database in view of the fact that it is difficult to find the new set of large item-sets in the whole updated database after an incremental database is added to the original database. This method of generating candidate item-sets is different from that of FUP and DMI. After generating the whole candidate item-sets, if each item-set in the whole candidate item-sets is large at an incremental database, the original database is scanned and the support of each item-set in the whole candidate item-sets is updated. So, the whole large item-sets in the whole updated database is found out. An updating algorithm that is suggested in this study does not use a pruning technique for reducing the database size in the update process. As a result, an updating algoritm that is suggested updates fast and efficiently discovered large item-sets.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.183-203
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• 1993

One of the most difficult and time-consuming stages in the development of the knowledge-based system is a knowledge acquisition. A splitting algorithm is developed to infer a rule-tree which can be converted to a rule-typed knowledge. A market segmentation may be performed in order to establish market strategy suitable to each market segment. As the sales data of a product market is probabilistic and noisy, it becomes necessary to prune the rule-tree-at an acceptable level while generating a rule-tree. A splitting algorithm is developed using the pruning measure based on a total amount of information gain and the measure of existing algorithms. A user can easily adjust the size of the resulting rule-tree according to his(her) preferences and problem domains. The algorithm is applied to a market segmentation problem of a medium-large computer market. The algorithm is illustrated step by step with a sales data of a computer market and is analyzed.

• 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%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.55-65
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• 2004

When a neural network is used to solve a given problem it is necessary to match the complexity of the network to that of the problem because the complexity of the network significantly affects its learning capability and generalization performance. Thus, it is desirable to have an algorithm that can find appropriate network structures in a self-organizing way. This paper proposes algorithms which automatically organize feed forward multi-layer neural networks with sigmoid hidden neurons for given problems. Using both constructive procedures and pruning procedures, the proposed algorithms try to find the near optimal network, which is compact and shows good generalization performance. The performances of the proposed algorithms are tested on four function regression problems. The results demonstrate that our algorithms successfully generate near-optimal networks in comparison with the previous method and the neural networks of fixed topology.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.30-33
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• 2016

With the advent of big data, data mining is more increasingly utilized in various decision-making fields by extracting hidden and meaningful information from large amounts of data. Even as exponential increase of the request of unrevealing the hidden meaning behind data, it becomes more and more important to decide to select which data mining algorithm and how to use it. There are several mainly used data mining algorithms in biology and clinics highlighted; Logistic regression, Neural networks, Supportvector machine, and variety of statistical techniques. In this paper it is attempted to compare the classification performance of an exemplary algorithm J48 and REPTree of ML algorithms. It is confirmed that more accurate classification algorithm is provided by the performance comparison results. More accurate prediction is possible with the algorithm for the goal of experiment. Based on this, it is expected to be relatively difficult visually detailed classification and distinction.