Learning with Drift Detection

Abstract. 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 distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to control the online error-rate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory guarantees that while the distribution is stationary, the error will decrease. When the distribution changes, the error will increase. The method controls the trace of the online error of the algorithm. For the actual context we define a warning level, and a drift level. A new context is declared, if in a sequence of examples, the error increases reaching the warning level at example kw, and the ...

• Type: Conference
• Year: 2004
• Where: SBIA
• Authors: João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues
• Keywords: Artificial Intelligence, Drift Level, Probability Distribution, SBIA 2004, Training Example