• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Journal of Information Technology Applications and Management
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• 제23권1호
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• 산업경영시스템학회지
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• 제39권1호
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• 대한산업공학회지
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• 제29권2호
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• pp.135-144
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• 2003

Traffic performance in a microcellular system is much more affected by cell dwell time and channel holding time in each cell. Cell dwell time of a call is characterized by its mobility pattern, i.e., stochastic changes of moving speed and direction. Cell dwell time provides important information for other analyses on traffic performance such as channel holding time, handover rate, and the average number of handovers per call. In the next generation mobile communication system, the cell size is expected to be much smaller than that of current one to accommodate the increase of user demand and to achieve high bandwidth utilization. As the cell size gets small, traffic performance is much more affected by variable mobility of users, especially by that of pedestrians. In previous work, analytical models are based on simple probability models. They provide sufficient accuracy in a simple second-generation cellular system. However, the role of them is becoming invalid in a picocellular environment where there are rapid change of network traffic conditions and highly random mobility of pedestrians. Unlike in previous work, we propose an improved probability model evolved from so-called Random walk model in order to mathematically formulate variable mobility of pedestrians and analyze the traffic performance. With our model, we can figure out variable characteristics of pedestrian mobility with stochastic correlation. The above-mentioned traffic performance measures are analyzed using our model.

• 응용통계연구
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• 제28권6호
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• pp.1113-1120
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• 2015

본 논문에서는 사회적으로나 개인적으로 매우 민감한 조사에서 세대별, 연령별 또는 계층별에 따라 조사하고자 하는 모집단이 여러 개의 층으로 구성되어 있고, 각 층이 다지속성으로 되어 있는 경우에, Abul-Ela 등의 다지모형과 Eriksson의 다지무관모형에서 사용한 단순임의추출법 대신에 층화추출법을 적용하여 각 층의 다지속성에 대한 모비율의 추정뿐만 아니라 모집단 전체 모비율에 대한 추정을 할 수 있는 층화 다지 확률화응답모형을 제안하였다. 그리고 층화 다지모형에 있어서 각 층의 표본배분에 대하여 비례배분과 최적배분을 고려하여 다루었다. 또한 층화 다지 확률화응답모형들간의 효율성을 비교해 본 결과 Eriksson의 다지무관모형이 Abul-Ela 등의 다지모형보다 효율적임을 알 수 있었다.

• 대한산업공학회지
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• 제19권1호
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• pp.73-77
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• 1993

This paper considers an Economic Order Quantity Model under random life cycle. It is assumed that the life cycle of the product is unknown; a random variable. Three cost parameters are considered; ordering cost, inventory carrying cost and salvage cost. Expected total cost is the optimization criterion. We show that the optimal cycle length is unique and finite, and present a simple line search method to find an optimal cycle length.

• Bulletin of the Korean Chemical Society
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• 제34권1호
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• pp.164-166
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• 2013

Using a simple random walk model, this study simulated the growth of a self-assembled monolayer (SAM) pattern generated by dip-pen nanolithography (DPN). In this model, the SAM pattern grew mainly via the serial pushing of molecules deposited from the tip. This study examined various SAM patterns, such as lines, crosses and letters, by changing the tip scan speed.

• 한국IT서비스학회지
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• 제14권4호
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• pp.121-135
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• 2015

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1998년도 춘계학술대회논문집; 용평리조트 타워콘도, 21-22 May 1998
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• pp.465-470
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• 1998

A cross-flow fan often generates discrete noise call blade passing frequency tones. Several methods have been investigated to reduce this BPF noise, where the random distribution of blades is the most promising one. A simple and effective algorithm to determine a random distribution of blades is proposed which considers fan. performance as well as noise characteristics. The proposed method is verified by a simple numerical model and is applied in manufacturing cross-flow fan samples. Also some experiments are carried out and the experimental results are analyzed.

• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.