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Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting’s relationship to support-vector machines. Some… (More)

- Bianca Zadrozny, John Langford, Naoki Abe
- ICDM
- 2003

We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conversion is based on cost-proportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in… (More)

- Naoki Abe, Hiroshi Mamitsuka
- ICML
- 1998

We address the problem of automatically acquiring case frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the efciency, our… (More)

- Naoki Abe, Manfred K. Warmuth
- Machine Learning
- 1990

We introduce a rigorous performance criterion for training algorithms for probabilistic automata (PAs) and hidden Markov models (HMMs), used extensively for speech recognition, and analyze the complexity of the training problem as a computational problem. The PA training problem is the problem of approximating an arbitrary, unknown source distribution by… (More)

- Naoki Abe, Bianca Zadrozny, John Langford
- KDD
- 2006

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an… (More)

- Naoki Abe, Bianca Zadrozny, John Langford
- KDD
- 2004

Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2)… (More)

- Andrew Arnold, Yan Liu, Naoki Abe
- KDD
- 2007

The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data, which raises the challenge of how best to leverage the temporal information for causal modeling. Recently graphical modeling with the concept of "Granger causality", based on the… (More)

- Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset
- Bioinformatics
- 2009

We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of 'Granger causality' to make assertions on… (More)

- Atsuyoshi Nakamura, Naoki Abe
- ICML
- 1998