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Wyszukujesz frazę "rule induction" wg kryterium: Temat

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    Wyświetlanie 1-5 z 5
    Tytuł:
    Tree-based induction of decision list from survival data
    Autorzy:
    Wróbel, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/333013.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    analiza przeżywalności
    lista decyzji
    zasada indukcji
    survival analysis
    survival trees
    decision list
    rule induction
    Opis:
    The paper presents an algorithm for induction of decision list from survival data. The algorithm uses a survival tree as the inner learner which is repeatedly executed in order to select the best rule at each iteration. The effectiveness of the algorithm was empirical tested for two implementations of survival trees on 15 benchmark datasets. The results show that proposed algorithm for survival decision list construction is able to induce more compact models than corresponding survival tree without the loss of the accuracy of predictions.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 20; 73-78
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reasoning algorithm for a creative decision support system integrating inference and machine learning
    Autorzy:
    Wilk-Kolodziejczyk, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/305355.pdf
    Data publikacji:
    2017
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    reasoning algorithm
    inferential theory of learning
    decision support
    rule induction
    logic of plausible reasoning
    Opis:
    In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.
    Źródło:
    Computer Science; 2017, 18 (3); 317-338
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough Set-Based Dimensionality Reduction for Supervised and Unsupervised Learning
    Autorzy:
    Shen, Q.
    Chouchoulas, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/908369.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    baza wiedzy
    gromadzenie wiedzy
    knowledge-based systems
    fuzzy rule induction
    rough dimensionality reduction
    knowledge acquisition
    Opis:
    The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality of datasets as a preprocessing step to training a learning system on the data. This paper investigates the utility of the Rough Set Attribute Reduction (RSAR) technique to both supervised and unsupervised learning in an effort to probe RSAR's generality. FuREAP, a Fuzzy-Rough Estimator of Algae Populations, which is an existing integration of RSAR and a fuzzy Rule Induction Algorithm (RIA), is used as an example of a supervised learning system with dimensionality reduction capabilities. A similar framework integrating the Multivariate Adaptive Regression Splines (MARS) approach and RSAR is taken to represent unsupervised learning systems. The paper describes the three techniques in question, discusses how RSAR can be employed with a supervised or an unsupervised system, and uses experimental results to draw conclusions on the relative success of the two integration efforts.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 583-601
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of rule induction to discover survival factors of patients after bone marrow transplantation
    Autorzy:
    Sikora, M.
    Wróbel, Ł.
    Mielcarek, M.
    Kałwak, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/333520.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    decision rules
    user-driven rule induction
    rule quality
    survival analysis
    bone marrow transplantation
    reguły decyzyjne
    zasada jakości
    analiza przeżywalności
    przeszczep szpiku kostnego
    Opis:
    Decision rules are commonly used tool for classification and knowledge discovery in data. The aim of this paper is to provide decision rule-based framework for analysis of survival data and apply it in mining of data describing patients after bone marrow transplantation. The paper presents a rule induction algorithm which uses sequential covering strategy and rule quality measures. An extended version of the algorithm gives the possibility of taking into account user’s requirements in the form of predefined rules and attributes which should be included in the final rule set. Additionally, in order to summarize the knowledge expressed by rule-based model, we propose the rule filtration algorithm which consists in selection of statistically significant rules describing the most disjoint parts of the entire data set. Selected rules are identified with so-called survival patterns. The survival patterns are rules which conclusions contain Kaplan-Meier estimates of survival function. In this way, the paper combines rule-based data classification and description with survival analysis. The efficiency of our method is illustrated with the analysis of data describing patients after bone marrow transplantation.
    Źródło:
    Journal of Medical Informatics & Technologies; 2013, 22; 35-53
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough set based processing of inconsistent information in decision analysis
    Autorzy:
    Słowiński, R.
    Stefanowski, J.
    Greco, S.
    Matarazzo, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/206765.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    klasyfikacja
    kombinatoryka
    teoria decyzji
    teoria gier
    classification
    decision analysis
    knowledge based systems
    multi-criteria decision analysis
    rough sets
    rule induction
    Opis:
    Inconsistent information is one of main difficulties in the explanation and recommendation tasks of decision analysis. We distinguish two kinds of such information inconsistencies : the first is related to indiscernibility of objects described by attributes defined in nominal or ordinal scales, and the other follows from violation of the dominance principle among attributes defined on preference ordered ordinal or cardinal scales, i.e. among criteria. In this paper we discuss how these two kinds of inconsistencies are handled by a new approach based on the rough sets theory. Combination of this theory with inductive learning techniques leads to generation of decision rules from rough approximations of decision classes. Particular attention is paid to numerical attribute scales and preference-ordered scales of criteria, and their influence on the syntax of induced decision rules.
    Źródło:
    Control and Cybernetics; 2000, 29, 1; 379-404
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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