Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy
classification. The approaches proposed so far to FDT learning, however, have generally …

Discretization algorithms have played an important role in data mining and knowledge
discovery. They not only produce a concise summarization of continuous attributes to help …

Ensembles of decision trees and neural networks are popular choices for solving
classification and regression problems. In this paper, a new ensemble of classifiers that …

Cost-sensitive learning is an important issue in both data mining and machine learning, in
that it deals with the problem of learning from decision systems relative to a variety of costs …

A key issue in decision tree (DT) induction with continuous valued attributes is to design an
effective strategy for splitting nodes. The traditional approach to solving this problem is …

In group-housed poultry, hormone and environment modulated variability in the processes
of follicle maturation and egg formation make it difficult to predict a daily egg-laying event …

The Wide-reaching usage of the standard called as IEEE 802.111 has been acting as a
solution to support aggressive network coverage with high bandwidth raised various security …

The decision‐tree (DT) algorithm is a very popular and efficient data‐mining technique. It is
non‐parametric and computationally fast. Besides forming interpretable classification rules …

Objective The desirable property of tools used to investigate biological data is easy to
understand models and predictive decisions. Decision trees are particularly promising in this …