On the monotonicity of the performance of Bayesian classifiers (Corresp.)

Waller, W.G.; Jain, Anil K.

doi:10.1109/tit.1978.1055877

Cited by 26 publications

(6 citation statements)

References 5 publications

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“…As the parameters are estimated from a limited number of instances, learning a separate multinet structure per class instead of one overall structure results in more unreliable parameter estimates and, hence, a higher probability of overgeneralization. This effect is closely related to the so-called peaking phenomenon, for a discussion see, e.g., [53].…”

Section: Multinet Bayesian Network Classifiersmentioning

confidence: 92%

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Baesens

Verstraeten

Poel

et al. 2004

European Journal of Operational Research

117

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Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customerÕs future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.

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Section: Multinet Bayesian Network Classifiersmentioning

confidence: 92%

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Baesens

Verstraeten

Poel

et al. 2004

European Journal of Operational Research

117

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“…However, most of these data sets were not highly informative, as judged by their ability to identify true-positive gene interactions with a low false-positive rate. Because it is known that many classifiers perform best when a subset of features are used, 37,38 we used only four informative microarray coexpression data sets for classification, [39][40][41][42] each showing a minimal AUC of 0.59. In total, these data sets contained 461 microarray hybridizations.…”

Section: Construction Of a Functional Gene Networkmentioning

confidence: 99%

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Franke

Bakel

Fokkens

et al. 2006

The American Journal of Human Genetics

443

391

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Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

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“…It is well known that trained classifiers suffer from the curse of dimensionality, which impedes generalization when the number of features becomes high. This so-called peaking phenomenon 51,52 implies an increasing difficulty in discerning discriminative from useless features as the dimensionality of the feature space increases. 53 The peaking phenomenon can prevent our scale selection algorithm from choosing the best set of scales.…”

Section: Discussionmentioning

confidence: 99%

Scale‐invariant segmentation of dynamic contrast‐enhanced perfusion MR images with inherent scale selection

Janssen¹,

Egmont-Petersen

Hendriks³

et al. 2002

J. Visual. Comput. Animat.

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Selection of the best set of scales is problematic when developing signal-driven approaches for pixel-based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best trade-off between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class-conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class-conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimized by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class-conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scale-invariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic magnetic resonance (MR) images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal-driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterize spatial properties of labelled images.

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On the monotonicity of the performance of Bayesian classifiers (Corresp.)

Cited by 26 publications

References 5 publications

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Scale‐invariant segmentation of dynamic contrast‐enhanced perfusion MR images with inherent scale selection

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