• Title/Summary/Keyword: selection

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Effects of selection index coefficients that ignore reliability on economic weights and selection responses during practical selection

  • Togashi, Kenji;Adachi, Kazunori;Yasumori, Takanori;Kurogi, Kazuhito;Nozaki, Takayoshi;Onogi, Akio;Atagi, Yamato;Takahashi, Tsutomu
    • Asian-Australasian Journal of Animal Sciences
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    • v.31 no.1
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    • pp.19-25
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    • 2018
  • Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

A study on the Predictors of criteria on Clothing Selection (의복선택기준 예측변인 연구)

  • Shin, Jeong-Won;Park, Eun-Joo
    • Journal of the Korean Society of Costume
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    • v.13
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    • pp.123-134
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    • 1989
  • The purpose of this study was to identify the predictable variables of criteria on clothing selection. Relationships among criteria on clothing selection, psychological variable, lifestyle variable, and demographic variable were tested by Pearsons' correlation coefficients and One-way ANOVA. The predictors of criteria on clothing selection were identified by Regression. The consumers were classified into several benefit-segments by criteria on clothing selection, and then, the character of each segment were identified by Multiple Discriminant Analysis. Data was obtained from 593 women living in Pusan by self-administered questionnaires. The results of the study were as follows; 1. Relationship between criteria on clothing selection and relative variables. 1) The important variables to criteria on clothing selection were "down-to-earth-sophisticated", "traditional-morden", "conventional-different", "conscientious-expendient", need for exhibitionism, need for sex, fashion / appearance. 2) The important factor of clothing selection criteria was comfort and it has significant difference among ages. 3) The higher of social-economic status have the more appearance-oriented selection. 2. Predictors of criteria on clothing selection. There were several important predictors of criteria on clothing selection like lifestyle, need, and self-image. Especially, fashion / appearance in lifestyle variable was very important. 3. Segmentation by the criteria on clothing selection. There are four groups Classified by the criteria on clothing selection, that is practical-oriented group, appearance-oriented group, practical and appearance-oriented group, and indifference group. The significant discriminative variables were Fashion / appearance factor, need for exhibitionism, and need for sex. The result of this study can be used for a enterprise to analysis the consumer and to build the strategy of advertisement clothing.

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Relay Selection Scheme Based on Quantum Differential Evolution Algorithm in Relay Networks

  • Gao, Hongyuan;Zhang, Shibo;Du, Yanan;Wang, Yu;Diao, Ming
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.7
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    • pp.3501-3523
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    • 2017
  • It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

Development of Interactive Feature Selection Algorithm(IFS) for Emotion Recognition

  • Yang, Hyun-Chang;Kim, Ho-Duck;Park, Chang-Hyun;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.4
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    • pp.282-287
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    • 2006
  • This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

Ensemble variable selection using genetic algorithm

  • Seogyoung, Lee;Martin Seunghwan, Yang;Jongkyeong, Kang;Seung Jun, Shin
    • Communications for Statistical Applications and Methods
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    • v.29 no.6
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    • pp.629-640
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    • 2022
  • Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

Evaluating the Performance of Four Selections in Genetic Algorithms-Based Multispectral Pixel Clustering

  • Kutubi, Abdullah Al Rahat;Hong, Min-Gee;Kim, Choen
    • Korean Journal of Remote Sensing
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    • v.34 no.1
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    • pp.151-166
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    • 2018
  • This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

A study of generation alternation model in genetic algorithm

  • Ito, Minoru;Sugisaka, Masanori
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.93.4-93
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    • 2002
  • When the GA is applied to optimization problems, it is important to maintain the diversity in designing generation alternation model. Generally, when the diversity is not fully maintained, it is difficult to find good solution, and it is easy to stagnate the early convergenece. In this paper, we propose the Elite Correlation Selection operator (ECS) as a new selection operator for survival. This selection operator aims to keep the diversity of populations and contributes the high searching ability. This selection operator is an extension of selection operator for survival in the Minimal Generation Gap (MGG). In the selection for survival, this selection operator selects one elite individual...

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Evaluating Variable Selection Techniques for Multivariate Linear Regression (다중선형회귀모형에서의 변수선택기법 평가)

  • Ryu, Nahyeon;Kim, Hyungseok;Kang, Pilsung
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.5
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    • pp.314-326
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    • 2016
  • The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

Modeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm

  • Sim, Kwee-Bo;Lee, Dong-Wook
    • International Journal of Control, Automation, and Systems
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    • v.1 no.4
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    • pp.453-458
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    • 2003
  • The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

Performance analysis of precoding-aided differential spatial modulation systems with transmit antenna selection

  • Kim, Sangchoon
    • ETRI Journal
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    • v.44 no.1
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    • pp.117-124
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    • 2022
  • In this paper, the performance of precoding-aided differential spatial modulation (PDSM) systems with optimal transmit antenna subset (TAS) selection is examined analytically. The average bit error rate (ABER) performance of the optimal TAS selection-based PDSM systems using a zero-forcing (ZF) precoder is evaluated using theoretical upper bound and Monte Carlo simulations. Simulation results validate the analysis and demonstrate a performance penalty < 2.6 dB compared with precoding-aided spatial modulation (PSM) with optimal TAS selection. The performance analysis reveals a transmit diversity gain of (NT-NR+1) for the ZF-based PDSM (ZF-PDSM) systems that employ TAS selection with NT transmit antennas, NS selected transmit antennas, and NR receive antennas. It is also shown that reducing the number of activated transmit antennas via optimal TAS selection in the ZF-PDSM systems degrades ABER performance. In addition, the impacts of channel estimation errors on the performance of the ZF-PDSM system with TAS selection are evaluated, and the performance of this system is compared with that of ZF-based PSM with TAS selection.