• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.212-216
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• 2024

Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

• Journal of Information Management
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• v.36 no.4
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• pp.51-69
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• 2005

High-speed classification method becomes an important research issue in text categorization systems. A fast text categorization technique, named feature value voting, is introduced recently on the text categorization problems. But the classification accuracy of this technique is not good as its classification speed. We present a novel approach for feature selection, named document-side feature selection, and apply it to feature value voting method. In this approach, there is no feature selection process in learning phase; but realtime feature selection is executed in classification phase. Our results show that feature value voting with document-side feature selection can allow fast and accurate text classification system, which seems to be competitive in classification performance with Support Vector Machines, the state-of-the-art text categorization algorithms.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1564-1569
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• 2009

Project managers play a key role in cost, time, and quality of a project. Selection of an appropriate project manager, therefore, is considered as one of the most important decisions in any construction project. It should be noted that most important decision makings are carried out by the project manager throughout the project. Traditionally, project manager selection in construction companies in Iran is through organizing an interview with candidates and selecting the most appropriate choice in accordance with the capabilities, potentials and individual specifications coupled with the requirements of the project. In the same direction, organizing interview on selection of appropriate candidate is usually carried out by senior managers of companies. Determination of the most important criteria for selection of project managers and also identification of significance coefficient of each criterion can highly help senior managers of companies to make sound selection decisions. In this paper, a numerical model has been considered for determination of significance of each criterion, details of which are submitted for selection of project manager in Iranian petrochemical, oil and gas sector companies. For this reason, all criteria- considered by senior managers of the companies under study- are first determined. Then, information obtained through 38 interviews, conducted by senior managers of the mentioned companies while selecting project manager, is analyzed. Significant coefficient of each criterion is calculated through the accumulated data using fuzzy curves method.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.159-165
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• 2007

In this paper, we propose a new pre-selection of candidate units that is suitable for the unit selection based Japanese TTS system. General pre-selection method performed by calculating a context-dependent cost within IP (Intonation Phrase). Different from other languages, however. Japanese has an accent represented as the height of a relative pitch, and several words form a single accentual phrase. Also. the prosody in Japanese changes in accentual phrase units. By reflecting such prosodic change in pre-selection. the qualify of synthesized speech can be improved. Furthermore, by calculating a context-dependent cost within accentual phrase, synthesis speed can be improved than calculating within intonation phrase. The proposed method defines AP. analyzes AP in context and performs pre-selection using accentual phrase matching which calculates CCL (connected context length) of the Phoneme's candidates that should be synthesized in each accentual phrase. The baseline system used in the proposed method is VoiceText, which is a synthesizer of Voiceware. Evaluations were made on perceptual error (intonation error, concatenation mismatch error) and synthesis time. Experimental result showed that the proposed method improved the qualify of synthesized speech. as well as shortened the synthesis time.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.481-496
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• 2012

Purpose: This paper aims at improving inefficiency of an existing posterior preference articulation method proposed for dual response surface optimization. The method generates a set of non-dominated solutions and then allows a decision maker (DM) to select the best solution among them through an interval selection strategy. Methods: This paper proposes an iterative posterior preference articulation method, which repeatedly generates the predetermined number of non-dominated solutions in an interval which becomes gradually narrower over rounds. Results: The existing method generates a good number of non-dominated solutions not used in the DM's selection process, while the proposed method generates the minimal number of non-dominated solutions necessitated in the selection process. Conclusion: The proposed method enables a satisfactory compromise solution to be achieved with minimal cognitive burden of the DM as well as with light computation load in generating non-dominated solutions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1394-1400
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• 1999

This paper presents and efficient improvement of the iterative eigenvalue calculation method and the selection of initial values in AESOPS algorithm. To determine the initial eigenvalues of the system, system state matrix is constructed with the two-axis generator model. From the submatrices including synchronous and damping coefficients, the initial eigenvalues are calculated by the QR method. Participation factors are also calculated from the above submatrices in order to determine the generators which have a important effect to the specific oscillation mode. Also, the heuristically approximated eigenvalue calculation method in the AESOPS algorithm is transformed to the Newton Raphson Method which is largely used in the nonlinear numerical analysis. The new methods are developed from the AESOPS algorithm and thus only a few calculation steps are added to practice the proposed algorithm.

• Nonlinear Functional Analysis and Applications
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• v.26 no.4
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• pp.811-837
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• 2021

In this paper, we propose a new conjugate gradient method for solving unconstrained optimization models. By using exact and strong Wolfe line searches, the proposed method possesses the sufficient descent condition and global convergence properties. Numerical results show that the proposed method is efficient at small, medium, and large dimensions for the given test functions. In addition, the proposed method was applied to solve practical application problems in portfolio selection.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.75-78
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• 1998

The selection criteria of the Authorized Stockage List(ASL) in the Army is based on Army Regulation(AR)409, the selection method of ASL is not considered in cost, weight and volume of repair parts. This paper is focused on developing for a new selection model taking account of cost, weight and volume of repair parts. This model is applied to data of a division. The ASL selected in the model is more reduced in cost, weight and volume than that of the previous method.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.177-183
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• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).