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Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the class-probability distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples.… (More)

- João Gama, Ricardo Rocha, Pedro Medas
- KDD
- 2003

In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. In this paper we extend the VFDT system in two directions: the ability to deal with… (More)

- João Gama, Pedro Medas, Pedro Pereira Rodrigues
- SAC
- 2005

This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, that works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting… (More)

- João Gama, Pedro Medas, Ricardo Rocha
- SAC
- 2004

This paper presents an hybrid adaptive system for induction of forest of trees from data streams. The Ultra Fast Forest Tree system (UFFT) is an incremental algorithm, with constant time for processing each example, works online, and uses the Hoeffding bound to decide when to install a splitting test in a leaf leading to a decision node. Our system has been… (More)

This paper presents the Ultra Fast Forest of Trees (UFFT) system. It is an incremental algorithm that works online, processing each example in constant time, and performing a single scan over the training examples. The system has been designed for numerical data. It uses analytical techniques to choose the splitting criteria, and the information gain to… (More)

- João Gama, Pedro Medas
- PRIS
- 2004

- Gladys Castillo, João Gama, Pedro Medas
- EPIA
- 2003

Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an extended period of time, the learning task can be complicated by changes in the distribution underlying the data. This problem is known in machine learning as concept drift. The… (More)

- Semeneh Addis, G N D'Ovidio, Pedro Medas
- Bollettino dell'Istituto sieroterapico milanese
- 1974

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