Conventional algorithms for decision tree induction use an attribute-value representation scheme for instances. This paper explores the empirical consequences of using set-valued ...
This paper investigates an extension of classification trees to deal with uncertain information where uncertainty is encoded in possibility theory framework. Class labels in data s...
Ilyes Jenhani, Nahla Ben Amor, Salem Benferhat, Zi...
Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data ...
Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods ar...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction algorithms have some limitations though, due to the typical strat...