Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model

Authors:
Jui-Sheng Chou;Min-Yuan Cheng;Yu-Wei Wu;Yian Tai
Affiliations:
Department of Construction Engineering, National Taiwan University of Science and Technology, 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan;Department of Construction Engineering, National Taiwan University of Science and Technology, 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan;Department of Construction Engineering, National Taiwan University of Science and Technology, 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan;Department of Chemical Engineering, National Taiwan University of Science and Technology, 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 17
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Adaptive Systems: An Introduction

Adaptive Systems: An Introduction
A Guide To The Project Management Body Of Knowledge (PMBOK Guides)

A Guide To The Project Management Body Of Knowledge (PMBOK Guides)
Application of support vector machines to corporate credit rating prediction

Expert Systems with Applications: An International Journal
Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

Expert Systems with Applications: An International Journal
An application of one-class support vector machines in content-based image retrieval

Expert Systems with Applications: An International Journal
A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets

Applied Soft Computing
Hierarchically SVM classification based on support vector clustering method and its application to document categorization

Expert Systems with Applications: An International Journal
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Expert Systems with Applications: An International Journal
A cost model to estimate the effort of data mining projects (DMCoMo)

Information Systems
Comparison of estimation methods of cost and duration in IT projects

Information and Software Technology
A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression

Expert Systems with Applications: An International Journal
Generalized linear model-based expert system for estimating the cost of transportation projects

Expert Systems with Applications: An International Journal
Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs

Expert Systems with Applications: An International Journal
Using support vector machine with a hybrid feature selection method to the stock trend prediction

Expert Systems with Applications: An International Journal
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine

Expert Systems with Applications: An International Journal

Preliminary cost estimates for thin-film transistor liquid-crystal display inspection and repair equipment: A hybrid hierarchical approach

Computers and Industrial Engineering
Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Accurately predicting fabricating cost in a timely manner can enhance corporate competitiveness. This study employs the Evolutionary Support Vector Machine Inference Model (ESIM) to predict the cost of manufacturing thin-film transistor liquid-crystal display (TFT-LCD) equipment. The ESIM is a hybrid model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM concerns primarily with learning and curve fitting, while the fmGA is focuses on optimization of minimal errors. Recently completed equipment development projects are utilized to assess prediction performance. The ESIM is developed to achieve the fittest C and @c parameters with minimized prediction error when used for cost estimate during conceptual stages. This study describes an actionable knowledge-discovery process using real-world data for high-tech equipment manufacturing industries. Analytical results demonstrate that the ESIM can predict the costs of manufacturing TFT-LCD fabrication equipment with sufficient accuracy.