• Title/Summary/Keyword: prediction accuracy

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Voting and Ensemble Schemes Based on CNN Models for Photo-Based Gender Prediction

  • Jhang, Kyoungson
    • Journal of Information Processing Systems
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    • v.16 no.4
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    • pp.809-819
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    • 2020
  • Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

The Prediction Ability of Genomic Selection in the Wheat Core Collection

  • Yuna Kang;Changsoo Kim
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2022.10a
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    • pp.235-235
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    • 2022
  • Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

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A Comparison of the Discrimination of Business Failure Prediction Models (부실기업예측모형의 판별력 비교)

  • 최태성;김형기;김성호
    • Journal of the Korean Operations Research and Management Science Society
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    • v.27 no.2
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    • pp.1-13
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    • 2002
  • In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

Prediction Accuracy Evaluation of Domain and Domain Combination Based Prediction Methods for Protein-Protein Interaction

  • Han, Dong-Soo;Jang, Woo-Hyuk
    • Bioinformatics and Biosystems
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    • v.1 no.2
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    • pp.128-133
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    • 2006
  • This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

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Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids

  • Le, Yiwen;He, Jinghan
    • Journal of Electrical Engineering and Technology
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    • v.12 no.3
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    • pp.1053-1063
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    • 2017
  • Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

Development of the Roundwood Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.95 no.2
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    • pp.203-208
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    • 2006
  • This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

Prediction of Parturition Day by Determination of Plasma Progesterone Concentrations in Companion Bitches 2. To Confirm the Accuracy of the Prediction of Parturition Day (반려견에서 혈중 Progesterone 농도 측정에 의한 분만일 예측 2. 분만예정일의 정확성 확인)

  • Lee, Ju Hwan;Son, Chang Ho
    • Journal of Veterinary Clinics
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    • v.37 no.6
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    • pp.305-310
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    • 2020
  • To confirm the accuracy of the prediction of parturition day, the actual parturition days were compared with each day of the prediction of parturition day (n = 80). The accuracy of the prediction of parturition day was 80.0% (64/80) with a precision of ± 2 days from the first day of estrus after the first vaginal discharge, 97.5% (78/80) from the day when plasma progesterone concentrations increase above 4.0 ng/ml, and 72.5% (58/80) from the first day of diestrus, respectively. The accuracy of the prediction of parturition day by plasma progesterone concentration was higher than that by the first day of estrus and diestrus after the first vaginal discharge. These results indicated that the determination of plasma progesterone concentrations at estrus were a useful method for estimating of parturition day and for the reproductive management in pregnant bitches.

A Study on Stock Trend Determination in Stock Trend Prediction

  • Lim, Chungsoo
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.35-44
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    • 2020
  • In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

Comparison and optimization of deep learning-based radiosensitivity prediction models using gene expression profiling in National Cancer Institute-60 cancer cell line

  • Kim, Euidam;Chung, Yoonsun
    • Nuclear Engineering and Technology
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    • v.54 no.8
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    • pp.3027-3033
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    • 2022
  • Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

A Study on Improving Prediction Accuracy by Modeling Multiple Similar Time Series (다중 유사 시계열 모델링 방법을 통한 예측정확도 개선에 관한 연구)

  • Cho, Young-Hee;Lee, Gye-Sung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.10 no.6
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    • pp.137-143
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    • 2010
  • A method for improving prediction accuracy through processing time series data has been studied in this research. We have designed techniques to model multiple similar time series data and avoided the shortcomings of single prediction model. We predicted the future changes by effective rules derived from these models. The methods for testing prediction accuracy consists of three types: fixed interval, sliding, and cumulative method. Among the three, cumulative method produced the highest accuracy.