• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.717-726
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• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.87-95
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• 2005

value prediction in high performance micro processors is a technique that exploits Instruction Level Parallelism(ILP) by predicting the outcome of an instruction and by breaking and executing true data dependences. In this paper, the mean Performance improvements by predictor according to a point of time for update of each table as well as prediction accuracy and Prediction rate are measured and assessed by comparison and analysis of value predictor that issues in parallel and run by predicting value, which is for Performance improvements of ILP in micro Processor. For the verification of its validity the SPECint95 benchmark through the simulation is compared by making use of execution driven system.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.279-285
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• 2016

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.1-10
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis, a model can be constructed with various machine learning algorithms, including decision tree. And feature importance has been utilized in selecting better input features that can improve performance of data analysis models for several application areas. In this paper, a method of utilizing feature importance calculated from the MDI method and its effects are investigated in the credit card customer churn prediction problem with classification trees. Compared with several random feature selections from case data, a set of input features selected from higher value of feature importance shows higher predictive power. It can be an efficient method for classifying and choosing input features necessary for improving prediction performance. The method organized in this paper can be an alternative to the selection of input features using feature importance in composing and using classification trees, including credit card customer churn prediction.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.1025-1037
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• 2020

This study attempted to evaluate the spatial characteristics and applicability of the predicted ensemble rainfall data used for heavy rain alarms. Limited area ENsemble prediction System (LENS) has 13 rainfall ensemble members, so it is possible to use a probabilistic method in issuing heavy rain warnings. However, the accessibility of LENS data is very low, so studies on the applicability of rainfall prediction data are insufficient. In this study, the evaluation index was calculated by comparing one point value and the area average value with the observed value according to the heavy rain warning system used for each administrative district. In addition, the accuracy of each ensemble member according to the LENS issuance time was evaluated. LENS showed the uncertainty of over or under prediction by member. Area-based prediction showed higher predictability than point-based prediction. In addition, the LENS data that predicts the upcoming 72-hour rainfall showed good predictive performance for rainfall events that may have an impact on a water disaster. In the future, the predicted rainfall data from LENS are expected to be used as basic data to prepare for floods in administrative districts or watersheds.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2610-2615
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• 2021

The prediction of irradiation-induced transition temperature shift for RPV steels is an important method for long term operation of nuclear power plant. Based on the irradiation embrittlement data, an irradiation-induced transition temperature shift prediction model is developed with machine learning method XGBoost. Then the residual, standard deviation and predicted value vs. measured value analysis are conducted to analyze the accuracy of this model. At last, Cu content threshold and saturation values analysis, temperature dependence, Ni/Cu dependence and flux effect are given to verify the reliability. Those results show that the prediction model developed with XGBoost has high accuracy for predicting the irradiation embrittlement trend of RPV steel. The prediction results are consistent with the current understanding of RPV embrittlement mechanism.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.169-179
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• 2021

This study shows that the two-stage k-means clustering method can improve prediction performance by predicting the stock price, To this end, this study introduces the two-stage k-means clustering algorithm and tests the prediction performance through comparison with various machine learning techniques. It selects the cluster close to the prediction target obtained from the k-means clustering, and reapplies the k-means clustering method to the cluster to search for a cluster closer to the actual value. As a result, the predicted value of this method is shown to be closer to the actual stock price than the predicted values of other machine learning techniques. Furthermore, it shows a relatively stable predicted value despite the use of a relatively small cluster. Accordingly, this method can simultaneously improve the accuracy and stability of prediction, and it can be considered as the new clustering method useful for small data. In the future, developing the two-stage k-means clustering is required for the large-scale data application.