Identification of the Compound Subjective Rule Interestingness Measure for Rule-Based Functional Description of Genes

Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rulesIn this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.

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STUDYING INTEREST MEASURES FOR ASSOCIATION RULES THROUGH A LOGICAL MODEL

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488510006404 ◽

2010 ◽

Vol 18 (01) ◽

pp. 87-106 ◽

Cited By ~ 13

Author(s):

MIGUEL DELGADO ◽

M. DOLORES RUIZ ◽

DANIEL SÁNCHEZ

Keyword(s):

Association Rules ◽

Logic Model ◽

Certainty Factor ◽

Logical Model ◽

Interestingness Measure ◽

Logic Approach ◽

Salient Features ◽

General Relations ◽

Rule Interestingness

Many papers have addressed the task of proposing a set of convenient axioms that a good rule interestingness measure should fulfil. We provide a new study of the principles proposed until now by means of the logic model proposed by Hájek et al.14 In this model association rules can be viewed as general relations of two itemsets quantified by means of a convenient quantifier.28 Moreover, we propose and justify the addition of two new principles to the three proposed by Piatetsky-Shapiro.27 We also use the logic approach for studying the relation between the different classes of quantifiers and these axioms. We define new classes of quantifiers according to the notions of strong and very strong rules, and we present a quantifier based on the certainty factor measure,317 studying its most salient features.

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An Interestingness Measure and Computation Method of Association Rules Based on Frequent Itemsets Relatedness

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.4039 ◽

2011 ◽

Vol 71-78 ◽

pp. 4039-4043

Author(s):

Xiang Chen ◽

Xue Feng Zhou ◽

Yong Zhang

Keyword(s):

Association Rules ◽

Association Rule ◽

Frequent Itemsets ◽

Computation Method ◽

Interestingness Measure ◽

Novel Method ◽

The Mean ◽

Rule Interestingness ◽

Selection Of ◽

Actual Relationship

To address inadequacy of association rules interestingness measure method currently, we present a novel method to measure interestingness with relatedness among items in frequent itemsets. It firstly computed relatedness between frequent k-itemsets and each subset of frequent 2-itemsets, which is a linear combination of Complementarity Intensity (CI), Substitutability Intensity (SI) and Mutual Interaction (MI). The mean of relatedness of all frequent 2-itemsets subsets was regarded as relatedness of frequent k-itemsets. Finally weighted computation method of association rule interestingness was given according to principle of objective interestingness of association rule is inversely proportional to relatedness of frequent itemsets. The method can not only sort rules, but also analyze actual relationship among all items in frequent 2-itemsets, which is conductive to selection of users on rules.

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Using Conventional Articulation Tests With Highly Unintelligible Children

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.2301.52 ◽

1992 ◽

Vol 23 (1) ◽

pp. 52-60 ◽

Cited By ~ 1

Author(s):

Pamela G. Garn-Nunn ◽

Vicki Martin

Keyword(s):

Test Results ◽

Phonological Processes ◽

Rule Based ◽

Severity Level ◽

Error Sensitivity ◽

Conventional Tests ◽

Severity Levels ◽

Conventional Test ◽

Impaired Children

This study explored whether or not standard administration and scoring of conventional articulation tests accurately identified children as phonologically disordered and whether or not information from these tests established severity level and programming needs. Results of standard scoring procedures from the Assessment of Phonological Processes-Revised, the Goldman-Fristoe Test of Articulation, the Photo Articulation Test, and the Weiss Comprehensive Articulation Test were compared for 20 phonologically impaired children. All tests identified the children as phonologically delayed/disordered, but the conventional tests failed to clearly and consistently differentiate varying severity levels. Conventional test results also showed limitations in error sensitivity, ease of computation for scoring procedures, and implications for remediation programming. The use of some type of rule-based analysis for phonologically impaired children is highly recommended.

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Haunted by a Doppelgänger

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000221 ◽

2014 ◽

Vol 61 (1) ◽

pp. 12-22 ◽

Cited By ~ 11

Author(s):

Bettina von Helversen ◽

Stefan M. Herzog ◽

Jörg Rieskamp

Keyword(s):

Relevant Information ◽

Psychological Research ◽

Decision Makers ◽

Human Judgment ◽

Rule Based ◽

New Employees ◽

High Performing ◽

Facial Similarity ◽

Similarity Effect ◽

Judgment Strategy

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

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