Evolutionary Decision Trees in Large-Scale Data Mining

This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied.

Sums up the authors research conducted over the last 15 years on the evolutionary induction of decision trees Discusses some basic elements from three domains are discussed, all of which are necessary to follow the proposed approach: evolutionary computations, decision trees, and parallel and distributed computing Presents in detail an evolutionary approach to the induction of decision trees

Zusammenfassung
"The structure of the book is well-thought-out. ... I recommend the book for students, researchers, and developers interested in real-life applications of big data analysis." (K. Balogh, Computing Reviews, February 15, 2021)

Inhalt

Evolutionary computation.- Decision trees in data mining.- Parallel and distributed computation.- Global induction of univariate trees.- Oblique and mixed decision trees.- Cost-sensitive tree induction.- Multi-test decision trees for gene expression data.- Parallel computations for evolutionary induction.