Publications

Publications by João Gama

1997

Probabilistic Linear Tree

Authors
Gama, J;

Publication
Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, July 8-12, 1997

Abstract

1996

Regression by Classification

Authors
Torgo, L; Gama, J;

Publication
Advances in Artificial Intelligence, 13th Brazilian Symposium on Artificial Intelligence, SBIA '96, Curitiba, Brazil, October 23-25, 1996, Proceedings

Abstract

2011

Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification

Authors
Cejudo, JMC; García, MB; Bueno, RM; Gama, J; Bifet, A;

Publication
Proceedings of the Second Workshop on Applications of Pattern Analysis, WAPA 2011, Castro Urdiales, Spain, October 19-21, 2011

Abstract

2000

Thesis: Inductive learning classification algorithms

Authors
Gama, J;

Publication
AI Commun.

Abstract

2011

Learning from medical data streams: An introduction

Authors
Rodrigues, PP; Pechenizkiy, M; Gaber, MM; Gama, J;

Publication
CEUR Workshop Proceedings

Abstract
Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference.

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2011

Learning decision rules from data streams

Authors
Gama, J; Kosina, P;

Publication
IJCAI International Joint Conference on Artificial Intelligence

Abstract
Decision rules, which can provide good interpretability and flexibility for data mining tasks, have received very little attention in the stream mining community so far. In this work we introduce a new algorithm to learn rule sets, designed for open-ended data streams. The proposed algorithm is able to continuously learn compact ordered and unordered rule sets. The experimental evaluation shows competitive results in comparison with VFDT and C4.5rules.

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