Evolutionary Optimization of Meta Data Metric for Method Recommendation

Abstract

Metalearning - a method for recommendation the most suitable data-mining algorithm to an unknown dataset - is an important problem that needs to be solved in order to design a completely autonomous data-mining solver. This paper deals with this particular problem by proposing a machinelearning method which recommends the most suitable algorithm to an unknown dataset based on the results of previous data-mining experiments.

The fundamental idea behind this is that the algorithms will perform similarly on similar datasets. The choice of datasets features - called meta data - is presented and the metric comparing datasets is optimized by means of evolutionary computation.

Keywords

• Algorithms
• Cybernetics
• Data mining
• Intelligent systems
• Data mining algorithm
• Evolutionary optimizations
• Machine-learning
• Metalearning