• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.305-312
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• 2006

K-means clustering algorithm 에서 주로 이루어지는 랜덤 초기화 (random initialization) 방법은 전역 최적화된 해(global minimum)를 찾아내기에 문제점을 지니고 있다. 즉, 여러 횟수의 알고리듬 반복(iteration)을 실행하더라도 전역 최적화된 해를 찾아내기가 매우 힘들며 주어진 자료의 크기(data size)가 큰 경우에 있어서 이는 거의 불가능하다. 본 논문은 이러한 문제점들을 극복하기 위한 방안으로, wavelet을 이용하여 최적의 초기 군집 중심점(initial clustering center)들을 선택하는 방법을 제시한다. 즉, 웨이블릿을 이용한 효과적인 초기화 (initialization)를 통해서 작은 알고리듬 반복 횟수만으로도 전역 최적화에 도달하는 초기화 방법을 기술한다. 이런 초기화 방법이 군집 알고리즘에 사용될 경우, 온라인상에서 실시간 이루어지는 군집 분석에 큰 도움이 된 수 있다.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.683-696
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• 2020

The aim of this research is to find an optimal gene set that provides highly accurate multi-class classification with a minimum number of genes. A two-stage procedure is proposed: Based on minimum redundancy and maximum relevance (mRMR) framework, several statistics to rank differential expression genes and K-means clustering to reduce redundancy between genes are used for data filtering procedure. And a particle swarm optimization is modified to select a small subset of informative genes. Two well known multi-class microarray data sets, ALL and SRBCT, are analyzed to indicate the effectiveness of this hybrid method.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.278-286
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• 2020

The prisoner's dilemma game which is a representative example of game theory is being studied with interest by many economists, social scientists, and computer scientists. In recent years, many researches on computational approaches that apply evolutionary computation techniques such as genetic algorithms and particle swarm optimization have been actively conducted to analyze prisoner dilemma games. In this study, we intend to evolve a strategy for a iterated prisoner dilemma game participating two or more players using three different binary particle swarm optimization techniques. As a result of experimenting by applying three kinds of binary particle swarm optimization to the iterated prisoner's dilemma game, it was confirmed that mutual cooperation can be established even among selfish participants to maximize their own gains. However, it was also confirmed that the more participants, the more difficult to establish a mutual cooperation relationship.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.371-373
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• 2023

factor 들이 많은 데이터의 군집화는 어려움을 요한다. K-means 클러스터링을 사용하여 군집화를 할 때, 각 데이터들이 가진 factor 의 개수가 상이한 경우 비슷한 성향을 가진 데이터임에도 불구하고 클러스터링이 적합하게 되지 않는 현상이 발생한다. 이러한 문제점을 해결하기 위해 최적의 군집화 개수를 결정하는 실루엣 기반 방법을 제안하고 제안기법의 성능을 평가한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.9-16
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• 2023

Robots are expected to expand their scope of application to the military field and take on important missions such as surveillance and enemy detection in the coming future warfare. Swarm robots can perform tasks that are difficult or time-consuming for a single robot to be performed more efficiently due to the advantage of having multiple robots. Swarm robots require mutual recognition and collaboration. So they send and receive vast amounts of data, making it increasingly difficult to verify SW. Hardware-in-the-loop simulation used to increase the reliability of mission verification enables SW verification of complex swarm robots, but the amount of verification data exchanged between the HILS device and the simulator increases exponentially according to the number of systems to be verified. So communication overload may occur. In this paper, we propose a digital twin-based communication optimization technique to solve the communication overload problem that occurs in mission verification of swarm robots. Under the proposed Digital Twin based Multi HILS Framework, Network DT can efficiently allocate network resources to each robot according to the mission scenario through the Network Controller algorithm, and can satisfy all sensor generation rates required by individual robots participating in the group. In addition, as a result of an experiment on packet loss rate, it was possible to reduce the packet loss rate from 15.7% to 0.2%.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.43-44
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• 2014

본 논문에서는 유전알고리즘의 유전자형-표현형을 사용한 수정된 이진 입자군집최적화의 두 번째 버전을 소개한다. 첫 번째 버전의 수정된 이진 입자군집최적화는 위치 정보에 유전학의 표현형을 사용한 반면에 제안하는 버전은 유전학의 유전자형을 사용한다. 이진 정보만을 제공하는 표현형에 비해 연속 공간 전체를 탐색공간으로 제공하는 유전자형 정보를 사용하여 해 공간을 보다 넓은 공간으로 만들 수 있다. 10개의 실험 평가 함수에 실험한 결과, 두 번째 버전은 탐색 공간이 넓고 지역최적해가 많은 함수에서 우수한 결과를 보였다.