2007
DOI: 10.1111/j.1468-0394.2007.00421.x
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A comparison of gradient ascent, gradient descent and genetic‐algorithm‐based artificial neural networks for the binary classification problem

Abstract: We compare log maximum likelihood gradient ascent, root-mean-square error minimizing gradient descent and genetic-algorithm-based artificial neural network procedures for a binary classification problem. We use simulated data and real-world data sets, and four different performance metrics of correct classification, sensitivity, specificity and reliability for our comparisons. Our experiments indicate that a genetic-algorithmbased artificial neural network that maximizes the total number of correct classificat… Show more

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Cited by 15 publications
(7 citation statements)
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“…Statistical methods include multiple discriminant analysis (MDA), logit regression and probit regression (Beaver, 1966;Altman, 1968;Ohlson, 1980). Artificial intelligence methods include casebased reasoning, inductive learning, artificial neural networks and knowledge-based expert systems (Dillard & Mutchker, 1987;Chung & Tam, 1992;Wong et al, 1995;Bryant, 1997;Pendharkar, 2007). Recently, the support vector machine (SVM) (Vapnik, 1998) has been introduced to financial applications, such as credit scoring, financial decisions and bankruptcy prediction (Tay & Cao, 2001;Shin et al, 2005;Gestel et al, 2006;Wu et al, 2007;Ding et al, 2008;Tsai, 2008).…”
Section: Introductionmentioning
confidence: 99%
“…Statistical methods include multiple discriminant analysis (MDA), logit regression and probit regression (Beaver, 1966;Altman, 1968;Ohlson, 1980). Artificial intelligence methods include casebased reasoning, inductive learning, artificial neural networks and knowledge-based expert systems (Dillard & Mutchker, 1987;Chung & Tam, 1992;Wong et al, 1995;Bryant, 1997;Pendharkar, 2007). Recently, the support vector machine (SVM) (Vapnik, 1998) has been introduced to financial applications, such as credit scoring, financial decisions and bankruptcy prediction (Tay & Cao, 2001;Shin et al, 2005;Gestel et al, 2006;Wu et al, 2007;Ding et al, 2008;Tsai, 2008).…”
Section: Introductionmentioning
confidence: 99%
“…For details refer to Chen [29] and Pendharkar [30]. To accelerate convergence, a momentum term can be added to the learning expressions.…”
Section: Methodsmentioning
confidence: 99%
“…This is especially true in non-stationarity systems where the performance data becomes stale long before sufficient data has been collected to build a reasonable model. For these scenarios, a "model free" approach can be implemented based on gradient ascent [17]. The only information stored by the forward model will be an estimate of the gradient derived from the most recent data points.…”
Section: Learning Backgroundmentioning
confidence: 99%