Leila Shila Shafti

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Constructive Induction is the process of transforming the original representation of hard concepts with complex interaction into a representation that highlights regularities. Most Constructive Induction methods apply a greedy strategy to find interacting attributes and then construct functions over them. This approach fails when complex interaction exists(More)
The aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. CI methods based on greedy search suffer from the local optima problem because of high variation in the search space of hard learning problems. To reduce the local optima(More)
The importance of preprocessing data before looking for patterns is greatest when data representation is primitive. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. Feature construction intends to create new features that encapsulate and highlight the hidden(More)