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8,022
Very simple classification rules perform well on most commonly used datasets
- Machine Learning
, 1993
"... The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest to the machin ..."
Abstract
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Cited by 547 (5 self)
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The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest
Integrating classification and association rule mining
- In Proc of KDD
, 1998
"... Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of di ..."
Abstract
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Cited by 578 (21 self)
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Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target
A Theory of Learning Classification Rules
, 1992
"... The main contributions of this thesis are a Bayesian theory of learning classification rules, the unification and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error whe ..."
Abstract
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Cited by 89 (6 self)
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The main contributions of this thesis are a Bayesian theory of learning classification rules, the unification and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error
Induction of Classification Rules
- Proceedings of International Conference on Rough Sets and Current Trends in Computing
, 2002
"... A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family o ..."
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A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family
Multiobjective Classification Rule Mining
"... Summary. In this chapter, we discuss the application of evolutionary multiob-jective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the an-tecedent and consequent parts of each rule are an interval v ..."
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Summary. In this chapter, we discuss the application of evolutionary multiob-jective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the an-tecedent and consequent parts of each rule are an interval
in Learning Classification Rules
, 1990
"... This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine lea.n_ng community that address issues of bias, learning as search, computational learning theory, Occam's razor, _universal " learning algori ..."
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This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine lea.n_ng community that address issues of bias, learning as search, computational learning theory, Occam's razor, _universal " learning
On threshold-based classification rules
- In Mathematical Statistics and Applications: Festschrift for Constance
, 2003
"... Abstract. Suppose we have n i.i.d. copies {(Xi, Yi), i = 1,..., n} of an example (X,Y), where X ∈ X is an instance and Y ∈ {−1, 1} is a label. A decision function (or classifier) f is a function f: X → [−1, 1]. Based on f, the example (X,Y) is misclassified if Y f(X) ≤ 0. In this paper, we first st ..."
Abstract
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Cited by 3 (3 self)
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maximum likelihood estimator. Next, we consider more complicated classification rules based on averaging over a class of base classifiers. We allow that certain examples are not classified due to lack of evidence, and provide a uniform bound for the margin. Moreover, we illustrate that when using averaged
Implication Strength of Classification Rules
"... Abstract. This paper highlights the interest of implicative statistics for classification trees. We start by showing how Gras ’ implication index may be defined for the rules derived from an induced decision tree. Then, we show that residuals used in the modeling of contingency tables provide intere ..."
Abstract
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Cited by 1 (0 self)
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Abstract. This paper highlights the interest of implicative statistics for classification trees. We start by showing how Gras ’ implication index may be defined for the rules derived from an induced decision tree. Then, we show that residuals used in the modeling of contingency tables provide
Learning Interestingness of Streaming Classification Rules
- Proceedings of 19th International Symposium on Computer and Information Sciences (ISCIS 2004
"... Abstract. Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathe ..."
Abstract
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Cited by 2 (1 self)
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Abstract. Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly
Selection of variables for cluster analysis and classification rules
"... “Selection of variables for cluster analysis and classification rules” ..."
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