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Knowledge Discovery from Data Streams (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

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In the last three decades, machine learning research and practice have focused on batch learning usually using small datasets. In batch learning, the whole training data is available to the algorithm, which outputs a decision model after processing the data eventually (or most of the times) multiple times. The rationale behind this practice is that examples are generated at random according to some stationary probability distribution. Most learners use a greedy, hill-climbing search in the space of models. They are prone to high-variance and over tting problems. Brain and Webb (2002) pointed out the relation between variance and data sample size. When learning from small datasets the main problem is variance reduction, while learning from large datasets may be more e ective when using algorithms that place greater emphasis on bias management.

In most challenging applications, learning algorithms act in dynamic environments, where the data are collected over time. A desirable property of these algorithms is the ability of incorporating new data. Some supervised learning algorithms are naturally incremental, for example k-nearest neighbors, and naive-Bayes. Others, like decision trees, require substantial changes to make incremental induction. Moreover, if the process is not strictly stationary (as are most real-world applications), the target concept could gradually change over time. Incremental learning is a necessary property but not suf- cient. Incremental learning systems must have mechanisms to incorporate concept drift, forgetting outdated data and adapting to the most recent state of nature.

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