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  • 1.
    Lindgren, Tony
    KTH, Superseded Departments, Computer and Systems Sciences, DSV.
    Methods for rule conflict resolution2004In: MACHINE LEARNING: ECML 2004, PROCEEDINGS / [ed] Boulicaut, JF; Esposito, F; Giannoti, F; Pedreschi, D, BERLIN: SPRINGER , 2004, Vol. 3201, p. 262-273Conference paper (Refereed)
    Abstract [en]

    When using unordered rule sets, conflicts can arise between the rules, i.e., two or more rules cover the same example but predict different classes. This paper gives a survey of methods used to solve this type of conflict and introduces a novel method called Recursive Induction. In total nine methods for resolving rule conflicts are scrutinised. The methods are explained in detail, compared and evaluated empirically on an number of domains. The results show that Recursive Induction outperforms all previously used methods.

  • 2.
    Lindgren, Tony
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    On handling conflicts between rules with numerical features2006In: Proc ACM Symp Appl Computing, 2006, p. 37-41Conference paper (Refereed)
    Abstract [en]

    Rule conflicts can arise in machine learning systems that utilise unordered rule sets. A rule conflict is when two or more rules cover the same example but differ in their majority classes. This conflict must be solved before a classification can be made. The standard methods for solving this type of problem are to use naive Bayes to solve the conflict or using the most frequent class (CN2). This paper studies the problem of rule conflicts in the area of numerical features. A novel family of methods, called distance based methods, for solving rule conflicts in continuous domains is presented. An empirical evaluation between a distance based method, CN2 and naive Bayes is made. It is shown that the distance based method significantly outperforms both naive Bayes and CN2.

  • 3.
    Lindgren, Tony
    et al.
    KTH, Superseded Departments, Computer and Systems Sciences, DSV.
    Boström, H.
    KTH, Superseded Departments, Computer and Systems Sciences, DSV.
    Resolving rule conflicts with double induction2004In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 8, no 5, p. 457-468Article in journal (Refereed)
    Abstract [en]

    When applying an unordered set of classification rules, the rules may assign more than one class to a particular example. Previous methods of resolving such conflicts between rules include using the most frequent class of the examples covered by the conflicting rules (as done in CN2) and using naïve Bayes to calculate the most probable class. An alternative way of solving this problem is presented in this paper: by generating new rules from the examples covered by the conflicting rules. These newly induced rules are then used for classification. Experiments on a number of domains show that this method significantly outperforms both the CN2 approach and naïve Bayes.

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