• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.631-636
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• 2006

In this paper, an algorithm of time series function forecasting using GMDH(group method of data handling) algorithm that gives more weight to the recent data is proposed. Traditional methods of GMDH forecasting gives same weights to the old and recent data, but by the point of view that the recent data is more important than the old data to forecast the future, an algorithm that makes the recent data contribute more to training is proposed for more accurate forecasting. The average error rate of electric power demand forecasting by the traditional GMDH algorithm which does not use data weighting algorithm is 0.9862 %, but as the result of applying the data weighting GMDH algorithm proposed in this paper to electric power forecasting demand the average error rate by the algorithm which uses data weighting algorithm and chooses the best data weighting rate is 0.688 %. Accordingly in forecasting the electric power demand by GMDH the proposed method can acquire the reduced error rate of 30.2 % compared to the traditional method.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.157-165
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• 2007

Typical meteorological data is fundamental to computer simulation introduced for environment-friendly architecture designs. Therefore, in order to improve accuracy of computer simulation, typical meteorological data should be established. By examining how to choose typical meteorological data, this study selected the optimized weight factor for TRY where weighting factor was not clearly set. As a result, the same weighting factor was applied to each climatic element and TRY data where the weight factor was applied could have the distribution very similar to measurement data. The weighting factor is considered to reflect geographical characteristics of Seoul and applied climatic elements.

• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.79-91
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• 2017

The meteorological data of 1 hour interval are required to write a typical weather data for building energy simulation. However, many meterological data are missing and the interpolation method to recover the missing data is required. Especially, lots of meterological data are replicated by linear interpolation method because the changes are not significant. While, the wind velocity fluctuates with the time or locations, so linear interpolation method is not appropriate in interpolation of the wind velocity data. In this study, three interpolation methods, using surrounding wind velocity data, Inverse Distance Weighting (IDW), Revised Inverse Distance Weighting (IDW-r), were analyzed considering the characteristics of wind velocity. The Revised Inverse Distance Weighting method, proposed in this study, showed the highest reliability in restoration of the wind velocity data among the analyzed methods.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.255-258
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• 2003

This paper defines Normalized Term Frequency Weighting method for automatic text categorization by using Box-Cox, and then it applies automatic text categorization. Box-Cox transformation is statistical transformation method which makes normalized data. This paper applies that and suggests new term frequency weighting method. Because Normalized Term Frequency is different from every term compared by existing term frequency weighting method, it is general method more than fixed weighting method such as log or root. Normalized term frequency weighting method's reasonability has been proved though experiments, used 8000 newspapers divided in 4 groups, which resulted high categorization correctness in all cases.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.741-747
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• 2011

Matrix Pencil method is one of the promising method to estimate DOA in non-stationary, multi-path coherent environment. Not only the Matrix Pencil Method offers better resolution than the conventional approach using covariance matrix, but also it is computationally very efficient. In this paper, we presented an effect of unbalanced data weighting in the formulation of the Matrix Pencil method. A new formulation has been suggested to mitigate the effect of unbalanced data weighting. Numerical simulation demonstrated that the proposed method can successfully eliminate the problem of unbalanced data weighting.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.775-786
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• 2002

In this paper, we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. The design and weighting effects were calculated for estimates produced from the 1998 small firm survey data. We considered the design and weighting effects on coefficients estimates of regression model using the design-based approach and the GEE approach.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.285-291
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• 2010

In this paper, we propose a new paradigm of weighting methods for naive Bayesian learning. We propose more fine-grained weighting methods, called value weighting method, in the context of naive Bayesian learning. While the current weighting methods assign a weight to an attribute, we assign a weight to an attribute value. We develop new methods, using Kullback-Leibler function, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general naive bayesian. The proposed method shows better performance in most of the cases.

• Geophysics and Geophysical Exploration
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• v.18 no.1
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• pp.9-13
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• 2015

All the resistivity data contain various kinds of noise. The major sources of noise in DC resistivity measurement are high contact resistance, measurement errors, and sporadic background noise. Thus, it is required to measure data noise to accurately interpret resistivity data. Reciprocal measurements can provide a measure of data precision and noise. In this study, we proposed a data-weighting method from reciprocity measurement. Furthermore, a data-weighting method using both the reciprocity error and data-misfit in the inversion process was studied. Applying the data-weighting method to the inversion of 3D resistivity data, it was confirmed that local anomalies are slightly suppressed in the final inversion results.