Abstract Machine learning (ML) is becoming an integral aspect of several domains in
medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such …

Increased interest in the opportunities provided by artificial intelligence and machine
learning has spawned a new field of health-care research. The new tools under …

This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Commonly used classification and regression tree methods like the CART algorithm are
recursive partitioning methods that build the model in a forward stepwise search. Although …

The research on the stock market prediction has been more popular in recent years.
Numerous researchers tried to predict the immediate future stock prices or indices based on …

This paper addresses the well-known classification task of data mining, where the objective
is to predict the class which an example belongs to. Discovered knowledge is expressed in …

This paper proposes a new constrained-syntax genetic programming (GP) algorithm for
discovering classification rules in medical data sets. The proposed GP contains several …

Decision tree classification provides a rapid and effective method of categorising datasets.
Many algorithmic methods exist for optimising decision tree structure, although these can be …

Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …