Artificial neural networks, classification trees and regression: Which method for which customer base?

Linder, Roland; Geier, Jeannine

doi:10.1057/palgrave.dbm.3240233

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…Research that has been conducted on the efficacy of RFM generally focuses on proprietary or judgmental models of RFM (e.g., Levin and Zavari, 2001;Magidson, 1988) and not on empirically based RFM models. More recently, research has moved away from RFM and has focused instead on newer, more sophisticated approaches to data-mining (c.f., Deichmann et al, 2002;Linder et al, 2004). The current study evaluates one popular, empirically based (as opposed to judgmental) approach to RFM.…”

Section: Introductionmentioning

confidence: 98%

Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

McCarty

Hastak

2007

Journal of Business Research

255

124

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Section: Introductionmentioning

confidence: 98%

Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

McCarty

Hastak

2007

Journal of Business Research

255

124

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“…Compared to MLR the ANN approach did slightly worse, which could be due to a non-optimal feature selection. (For a more general comparison between MLR and ANN please refer elsewhere) [16,17]. Further aspects not yet considered are data cleaning, missing data, and the effect of incomplete data on MLR and ANN.…”

Section: Resultsmentioning

confidence: 99%

Computer‐Aided Diagnosis of Multiple Sclerosis

Linder

Mörschner

Pöppl

et al. 2008

Computational and Mathematical Methods in Medicine

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The study aims to develop a computer-assisted decision support based on cerebrospinal fluid (CSF) and blood findings to improve their value and ease the diagnostic procedure of chronic inflammatory diseases (CIDs) of central nervous system (CNS). Data were collected from patients suffering from multiple sclerosis (MS, n ¼ 73), from another CID of the CNS (n ¼ 22), or a psychiatric disease (control group, CTRL, n ¼ 12). Univariate and multivariate analyses were performed using multiple logistic regression and artificial neural networks. Differentiating between MS and CID, no parameter could be disclosed that could provide a meaningful decision support. However, multivariate analysis obtained a statistically significant classification (sensitivity ¼ 84.9%, specificity ¼ 54.5%, p , 0.001). On the contrary, multivariate analysis based on the differentiation between MS vs. CTRL, gave good results (sensitivity ¼ 95.9%, specificity ¼ 83.3%, p , 0.001). It became evident from standard laboratory findings that there is a significant potential for computer-aided decision support.

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“…can be found in the works of e.g. Khemakhem and Boujelbènea (2015), Karaa and Krichene (2012), Huang et al (2004), Oreski et al (2012), Ogwueleka et al (2015), Linder et al (2004) …”

Section: Literature Reviewmentioning

confidence: 99%

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Piasecki

Wójcicka-Wójtowicz

2017

Folia Oeconomica Stetinensia

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Identifying potential healthy and unsound customers is an important task. The reduction of loans granted to companies of questionable credibility can influence banks' performance. A prior identification of factors that affect the condition of companies is a vital element. Among the most commonly used methods we can enumerate discriminant analysis (DA), scoring methods, neural networks (NN), etc. This paper investigates the use of different structure NN and DA in the process of the classification of banks' potential clients. The results of those different methods are juxtaposed and their performance compared.

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Artificial neural networks, classification trees and regression: Which method for which customer base?

Cited by 22 publications

References 16 publications

Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

Computer‐Aided Diagnosis of Multiple Sclerosis

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

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