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) In the first place, we could try to stick to the standard way of completing a possibility measure from a basic possibility assignment, namely by taking the maximum over the elementary events However, this seems to force us to accept the requirement that the focal sets of the random set must be consonant In our opinion this requirement is entirely unacceptable We cannot think of an application in which this requirement is actually met (Symmetric confidence levels on statistically estimated parameters may be the only exception, but to handle these, statistical methods should be preferred) Nevertheless, this is the approach underlying the so-called mass assignment theory [Baldwin et al 1995], which we discuss briefly in the next section, and which has been fairly popular.



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It does not support drawing inferences However, Microsoft Research also offers MSBN (Microsoft Bayesian Networks), a tool for (manually) building Bayesian networks and drawing inferences with them, MSBN comes with a graphical user interface Both programs, WinMine as well as MSBN, are available for Windows only..

In passing we mention that a very simple argument put forth in favor of the maximum operation in [Gebhardt 1997], namely that we should choose it because it is the most pessimistic choice possible, is hardly acceptable This argument overlooks that a possibility measure in the interpretation of the context model is an upper bound on the underlying probability measure and that degrees of possibility model negative information That is, the more we know about the experimental setup, the tighter the bound on the probability will be (see above) But by choosing the maximum operation, we make.





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When 3GPP was created, four Technical Speci cation Groups (TSGs) were formed to undertake the preparation of technical speci cations. The four TSGs were as follows:

Roles, identities, and sexual orientation: Homosexuality, heterosexuality, and bisexuality In R F Levant & W S Pollack (Eds), A new psychology of men (pp 359 382) New York: Basic Books Hart, L E (1981) An investigation of the effect of male therapists views of women on the process and outcome of therapy with women Dissertation Abstracts International, 42, 2529B Hartung, C M, & Widiger, T A (1998) Gender differences in the diagnosis of mental disorders: Conclusions and controversies of the DSM-IV Psychological Bulletin, 213, 260 278 Harway, M, & Hansen, M (1994) Spouse abuse: Treating battered women, batterers and their children Sarasota, FL: Professional Resource Press Helms, J E, & Cook, D A (1999) Using race and culture in counseling and psychotherapy: Theory and process Boston: Allyn & Bacon..

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In the lter feature selection methods, various statistical measures are used to rank genes by their discriminative powers. Successful measures include using the t-test, the chi-square test, information gain, and the Kruskal Wallis test. A recently proposed biomarker identi cation approach involves clustering gene expression pro les [58]. In such an approach, genes are clustered based on their microarray expression pro les. Then, within each cluster, the most representative gene is selected (the representative gene could be the gene closest to the mean or median expression value within the cluster). The representative genes are collected and used as selected features to build a predictor for classi cation of unknown samples. However, selected sets of genes often lack biological justi cation and their size is usually too large for experimental validation. 4.3.6.2 Domain Knowledge-Based Feature Selection A recently proposed feature selection approach exploits the biological knowledge of gene functions as a criterion for selection [59]. The underlying hypothesis for this approach is that the difference between samples lies in a few key gene functions. Genes annotated with those key functions are likely to be very useful for classi cation. To use this observation, a statistical test is applied to microarray data in order to rank genes by their p-values and generate a subset of signi cant genes. Selected genes are compared to the overall population in order to identify the most signi cant function. Only genes associated with the most signi cant function are selected for classi cation. This approach results in a small set of genes that provide high accuracy (see the case study below).

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