Guest editorial data mining and knowledge discovery with evolutionary algorithms

DATA mining (DM) consists of extracting interesting knowledge from real-world, large and complex data sets; and is the core step of a broader process, called knowledge discovery from databases (KDD). In addition to the DM step, which actually extracts knowledge from data, KDD process includes several preprocessing (data preparation) and postprocessing (knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting for the user. The total process is computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several machine learning related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions. Intuitively, the global search performed by EAs can more effectively discover interesting patterns that would have been missed by the greedy search performed by many KDD methods. At present, results on investigations integrating EAs and DM, both theory and applications, are being made available in different journals and conference proceedings mainly in the fields dedicated to knowledge discovery and data mining or evolutionary computing. The objective of this issue is to assemble a set of high-quality original contributions that reflect the advances and the state-of-the-art in the area of data mining and knowledge discovery with EAs, thereby presenting a consolidated view to the interested researchers in the aforesaid fields, in general, and readers of the journal IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, in particular. The special issue emphasizes the utility of different evolutionary computing tools to various facets of KDD. The issue has four papers. First two papers are on classification, while the third paper is on knowledge discovery from text. The fourth paper gives a comparison of various evolutionary and non-EAs for prototype selection and training set selection for data reduction in KDD. Experts of different active groups from the United States, Hong Kong, the United Kingdom, and Spain have written these articles; and 3–5 referees review each of them. These four papers were chosen from a set of 35 submissions for this issue. Let us scan these papers.