![]() Then the selected instances are rejoined in a new training set and the same procedure, partitioning and application of an instance selection algorithm, is repeated. ![]() Our method divides the original training set into small subsets where the instance selection algorithm is applied. This paper presents a divide-and-conquer recursive approach to the problem of instance selection for instance based learning for very large problems. When we face huge problems, scalability is an issue, and most algorithms are not applicable. In the best case, these algorithms are of efficiency O( n 2), n being the number of instances. However, although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is of hundreds of thousands or millions. Instance selection is becoming more and more relevant due to the huge amount of data that is being constantly produced. ![]()
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