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  • 1
    Electronic Resource
    Electronic Resource
    Unifying instance-based and rule-based induction (1996)
    Springer
    Machine learning 24 (1996), S. 141-168 
    Details
    ISSN: 0885-6125
    Keywords: Concept learning ; multi-strategy learning ; rule induction ; instance-based learning ; nearest-neighbor classification ; case-based reasoning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Several well-developed approaches to inductive learning low exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem combining multiple methods in one algorithm. This article describes a unification of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally specific rules, and classification is oerformed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be efficient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that eachoof RISE's components is essential to this performance. Most significantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a significant synergy can be obtained by combining multiple empirical methods.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00058656
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (1997)
    Springer
    Machine learning 29 (1997), S. 103-130 
    Details
    ISSN: 0885-6125
    Keywords: Simple Bayesian classifier ; naive Bayesian classifier ; zero-one loss ; optimal classification ; induction with attribute dependences
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007413511361
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Unifying Instance-Based and Rule-Based Induction (1996)
    Springer
    Machine learning 24 (1996), S. 141-168 
    Details
    ISSN: 0885-6125
    Keywords: Concept learning ; multi-strategy learning ; rule induction ; instance-based learning ; nearest-neighbor classification ; case-based reasoning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a unification of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally specific rules, and classification is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be efficient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that each of RISE‘s components is essential to this performance. Most significantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a significant synergy can be obtained by combining multiple empirical methods.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018006431188
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    Paper (German National Licenses)
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