Results 1  10
of
114
The CN2 Induction Algorithm
 MACHINE LEARNING
, 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
Abstract

Cited by 885 (6 self)
 Add to MetaCart
(Show Context)
Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the cn2, id3 and aq algorithms are compared on three medical classification tasks.
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
Abstract

Cited by 432 (17 self)
 Add to MetaCart
In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting
Induction of Selective Bayesian Classifiers
 CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space ..."
Abstract

Cited by 262 (7 self)
 Add to MetaCart
In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decisiontree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.
Concept learning and the problem of small disjuncts
 In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence
, 1989
"... Ideally, de nitions induced from examples should consist of all, and only, disjuncts that are meaningful (e.g., as measured by a statistical signi cance test) and have alowerror rate. Existing inductive systems create de nitions that are ideal with regard to large disjuncts, but far from ideal with ..."
Abstract

Cited by 166 (2 self)
 Add to MetaCart
Ideally, de nitions induced from examples should consist of all, and only, disjuncts that are meaningful (e.g., as measured by a statistical signi cance test) and have alowerror rate. Existing inductive systems create de nitions that are ideal with regard to large disjuncts, but far from ideal with regard to small disjuncts, where a small (large) disjunct is one that correctly classi es few (many) training examples. The problem with small disjuncts is that many ofthemhavehigh rates of misclassi cation, and it is di cult to eliminate the errorprone small disjuncts from a de nition without adversely a ecting other disjuncts in the de nition. Various approaches to this problem are evaluated, including the novel approach ofusing a bias di erent than the \maximum generality " bias. This approach, and some others, prove partly successful, but the problem of small disjuncts remains open. Support for this researchwas provided by the Army Research O Science Foundation under grant IRI8620052. ce under grantARODAAG2984K0060 and the National 1 The Problem of Small Disjuncts Systems that learn from examples do not usually succeed in creating a purely conjunctive
Incremental Reduced Error Pruning
, 1994
"... This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming , most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning , is proposed that attempts to address all of these problems. Experiments show that in many noisy domains t ..."
Abstract

Cited by 152 (23 self)
 Add to MetaCart
This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming , most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning , is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. OEFAITR9409 1 Introduction Being able to deal with noisy data is a must for algorithms that are meant to learn concepts in realworld domains. Significant effort has gone into investigating the effect of noisy data on decision tree learning algorithms (see e.g. [Quinlan, 1993, Breiman et al., 1984]). Not surprisingly, noise handling methods have also entered the emerging field of Inductive Logic Programming (ILP) [Muggleton, 1992]. Linus [Lavr...
Learning classification trees
 Statistics and Computing
, 1992
"... Algorithms for learning cIassification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. T ..."
Abstract

Cited by 145 (8 self)
 Add to MetaCart
Algorithms for learning cIassification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to QuinIan’s information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan’s C4 (1987) and Breiman et aL’s CART (1984) show the full Bayesian algorithm produces more accurate predictions than versions
Theoretical and Empirical Analysis of ReliefF and RReliefF
, 2003
"... Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While t ..."
Abstract

Cited by 124 (1 self)
 Add to MetaCart
Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming. A broad spectrum of successful uses calls for especially careful investigation of various features Relief algorithms have. In this paper we theoretically and empirically investigate and discuss how and why they work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.
A Comparative Analysis of Methods for Pruning Decision Trees
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... In this paper, we address the problem of retrospectively pruning decision trees induced from data, according to a topdown approach. This problem has received considerable attention in the areas of pattern recognition and machine learning, and many distinct methods have been proposed in literature. ..."
Abstract

Cited by 117 (2 self)
 Add to MetaCart
(Show Context)
In this paper, we address the problem of retrospectively pruning decision trees induced from data, according to a topdown approach. This problem has received considerable attention in the areas of pattern recognition and machine learning, and many distinct methods have been proposed in literature. We make a comparative study of six wellknown pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their formulation. Comments on the characteristics of each method are empirically supported. In particular, a wide experimentation performed on several data sets leads us to opposite conclusions on the predictive accuracy of simplified trees from some drawn in the literature. We attribute this divergence to differences in experimental designs. Finally, we prove and make use of a property of the reduced error pruning method to obtain an objective evaluation of the tendency to overprune/underprune ob...
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

Cited by 88 (6 self)
 Add to MetaCart
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 when learning logical rules. The thesis is motivated by considering some current research issues in machine learning such as bias, overfitting and search, and considering the requirements placed on a learning system when it is used for knowledge acquisition. Basic Bayesian decision theory relevant to the problem of learning classification rules is reviewed, then a Bayesian framework for such learning is presented. The framework has three components: the hypothesis space, the learning protocol, and criteria for successful learning. Several learning protocols are analysed in detail: queries, logical, noisy, uncertain and positiveonly examples. The analysis is done by interpreting a protocol as a...
Classification trees with unbiased multiway splits
 Journal of the American Statistical Association
, 2001
"... Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods i ..."
Abstract

Cited by 73 (11 self)
 Add to MetaCart
Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in number of missing values. This is an advantage because inferences from the tree structures can be adversely affected by selection bias. The new methods are shown to be highly competitive in terms of computational speed and classification accuracy of future observations. Key words and phrases: Decision tree, linear discriminant analysis, missing value, selection bias. 1