The Experts below are selected from a list of 56145 Experts worldwide ranked by ideXlab platform
Christophe Mues - One of the best experts on this subject based on the ideXlab platform.
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Improving a neuro-fuzzy classifier using Exploratory Factor Analysis
International Journal of Intelligent Systems, 2020Co-Authors: Jurgen Martens, Geert Wets, Jan Vanthienen, Christophe MuesAbstract:As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an Exploratory Factor Analysis. We compare the algorithms before and after applying an Exploratory Factor Analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base.
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Improving a neuro-fuzzy classifier using Exploratory Factor Analysis
International Journal of Intelligent Systems, 2000Co-Authors: Jurgen Martens, Geert Wets, Jan Vanthienen, Christophe MuesAbstract:As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an Exploratory Factor Analysis. We compare the algorithms before and after applying an Exploratory Factor Analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base. (C) 2000 John Wiley & Sons, Inc.
Jurgen Martens - One of the best experts on this subject based on the ideXlab platform.
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Improving a neuro-fuzzy classifier using Exploratory Factor Analysis
International Journal of Intelligent Systems, 2020Co-Authors: Jurgen Martens, Geert Wets, Jan Vanthienen, Christophe MuesAbstract:As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an Exploratory Factor Analysis. We compare the algorithms before and after applying an Exploratory Factor Analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base.
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Improving a neuro-fuzzy classifier using Exploratory Factor Analysis
International Journal of Intelligent Systems, 2000Co-Authors: Jurgen Martens, Geert Wets, Jan Vanthienen, Christophe MuesAbstract:As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an Exploratory Factor Analysis. We compare the algorithms before and after applying an Exploratory Factor Analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base. (C) 2000 John Wiley & Sons, Inc.
Kehai Yuan - One of the best experts on this subject based on the ideXlab platform.
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on the likelihood ratio test for the number of Factors in Exploratory Factor Analysis
Structural Equation Modeling, 2007Co-Authors: Kentaro Hayashi, Peter M Bentler, Kehai YuanAbstract:In the Exploratory Factor Analysis, when the number of Factors exceeds the true number of Factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of Factors may be incorrect. Several researchers have pointed out this phenomenon, but it is not well known among applied researchers who use Exploratory Factor Analysis. We demonstrate that overFactoring is one cause for the well-known fact that the likelihood ratio test tends to find too many Factors.
Bjt Morgan - One of the best experts on this subject based on the ideXlab platform.
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principal component Analysis and Exploratory Factor Analysis
Statistical Methods in Medical Research, 1992Co-Authors: I T Joliffe, Bjt MorganAbstract:In this paper we compare and contrast the objectives of principal component Analysis and Exploratory Factor Analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to Factor Analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.
Brian D. Haig - One of the best experts on this subject based on the ideXlab platform.
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Oxford Scholarship Online - Exploratory Factor Analysis
Oxford Scholarship Online, 2018Co-Authors: Brian D. HaigAbstract:Chapter 6 argues that Exploratory Factor Analysis is an abductive method of theory generation that exploits a principle of scientific inference known as the principle of the common cause. Factor Analysis is an important family of multivariate statistical methods that is widely used in the behavioral and social sciences. The best known model of Factor Analysis is common Factor Analysis, which has two types: Exploratory Factor Analysis and confirmatory Factor Analysis. A number of methodological issues that arise in critical discussions of Exploratory Factor Analysis are considered. It is suggested that Exploratory Factor Analysis can be profitably employed in tandem with confirmatory Factor Analysis.
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Exploratory Factor Analysis theory generation and scientific method
Multivariate Behavioral Research, 2005Co-Authors: Brian D. HaigAbstract:This article examines the methodological foundations of Exploratory Factor Analysis (EFA) and suggests that it is properly construed as a method for generating explanatory theories. In the first half of the article it is argued that EFA should be understood as an abductive method of theory generation that exploits an important precept of scientific inference known as the principle of the common cause. This characterization of the inferential nature of EFA coheres well with its interpretation as a latent variable method. The second half of the article outlines a broad theory of scientific method in which abductive reasoning figures prominently. It then discusses a number of methodological features of EFA in the light of that method. Specifically, it is argued that EFA helps researchers generate theories with genuine explanatory merit; that Factor indeterminacy is a methodological challenge for both EFA and confirmatory Factor Analysis, but that the challenge can be satisFactorily met in each case; and, tha...
