Results 1 - 10 of 4,634

The CN2 Induction Algorithm

by Peter Clark , Tim Niblett - 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 890 (6 self) - Add to MetaCart
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

The Case Against Accuracy Estimation for Comparing Induction Algorithms

by Foster Provost, Tom Fawcett, Ron Kohavi - In Proceedings of the Fifteenth International Conference on Machine Learning , 1997
"... We analyze critically the use of classification accuracy to compare classifiers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learning algorithms, and standard benchmark data sets. The results raise serious concerns about the use of accuracy for compar ..."
Abstract - Cited by 414 (23 self) - Add to MetaCart
We analyze critically the use of classification accuracy to compare classifiers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learning algorithms, and standard benchmark data sets. The results raise serious concerns about the use of accuracy

Multiple Comparisons in Induction Algorithms

by David Jensen, Paul R. Cohen - MACHINE LEARNING , 1998
"... A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We ..."
Abstract - Cited by 94 (10 self) - Add to MetaCart
A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We

Inductive learning algorithms and representations for text categorization,”

by Susan Dumais , John Platt , Mehran Sahami , David Heckerman - in Proceedings of the International Conference on Information and Knowledge Management, , 1998
"... ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text ..."
Abstract - Cited by 652 (8 self) - Add to MetaCart
ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text

Fast Effective Rule Induction

by William W. Cohen , 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error r ..."
Abstract - Cited by 1274 (21 self) - Add to MetaCart
Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error

Induction of Decision Trees

by J. R. Quinlan - MACH. LEARN , 1986
"... The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such syste ..."
Abstract - Cited by 4377 (4 self) - Add to MetaCart
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one

Irrelevant Features and the Subset Selection Problem

by George H. John, Ron Kohavi, Karl Pfleger - MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL , 1994
"... We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features ..."
Abstract - Cited by 757 (26 self) - Add to MetaCart
We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features

Wrappers for Feature Subset Selection

by Ron Kohavi, George H. John - AIJ SPECIAL ISSUE ON RELEVANCE , 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
Abstract - Cited by 1569 (3 self) - Add to MetaCart
families of induction algorithms used: decision trees and Naive-Bayes.

A Bayesian method for the induction of probabilistic networks from data

by Gregory F. Cooper, EDWARD HERSKOVITS - MACHINE LEARNING , 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract - Cited by 1400 (31 self) - Add to MetaCart
of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief

Supervised and unsupervised discretization of continuous features

by James Dougherty, Ron Kohavi, Mehran Sahami - in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference , 1995
"... Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de n-ing characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised dis ..."
Abstract - Cited by 540 (11 self) - Add to MetaCart
-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm signi cantly improved if features were discretized in advance � in our experiments, the performance never signi cantly degraded, an interesting phenomenon considering the fact that C4
Results 1 - 10 of 4,634