#### Filter Results:

- Full text PDF available (86)

#### Publication Year

1983

2017

- This year (2)
- Last 5 years (18)
- Last 10 years (30)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Ron Kohavi, George H. John
- Artif. Intell.
- 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 feature subset selection method should consider how the… (More)

- Ron Kohavi
- IJCAI
- 1995

We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive… (More)

- Eric Bauer, Ron Kohavi
- Machine Learning
- 1999

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and… (More)

- James Dougherty, Ron Kohavi, Mehran Sahami
- ICML
- 1995

Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de ning characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based… (More)

- George H. John, Ron Kohavi, Karl Pfleger
- ICML
- 1994

We address the problem of nding a subset of features that allows a supervised induc tion algorithm to induce small high accuracy concepts We examine notions of relevance and irrelevance and show that the de nitions used in the machine learning literature do not adequately partition the features into useful categories of relevance We present de ni tions for… (More)

- Ron Kohavi
- KDD
- 1996

Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classii-cation tasks even when the conditional independence assumption on which they are based is violated. However , most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision… (More)

- Ron Kohavi, David Wolpert
- ICML
- 1996

We present a bias variance decomposition of expected misclassi cation rate the most commonly used loss function in supervised classi cation learning The bias variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms yet no decomposition was o ered for the more commonly used zero one… (More)

- Ron Kohavi
- ECML
- 1995

We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on arti cial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can… (More)

- Zijian Zheng, Ron Kohavi, Llew Mason
- KDD
- 2001

This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset. The experimental results confirm the performance improvements previously claimed by the authors on the artificial data, but some of these gains do not carry over to the real datasets, indicating overfitting of the algorithms to the IBM… (More)

- Foster J. Provost, Tom Fawcett, Ron Kohavi
- ICML
- 1998

We analyze critically the use of classi cation accuracy to compare classi ers 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 comparing classi ers and draw into question the conclusions that… (More)