Metalearning for Algorithm Recommendation: an Introduction

Abstract

Data mining applications normally involve preparation of a dataset that can be processed by a learning algorithm (Figure 2.1). Given that there are usually several algorithms available, the user must select one of them. Additionally, most algorithms have parameters which must be set, so, after choosing the algorithm, the user must decide the values for each one of its parameters. The choice of algorithm is guided by some kind of metaknowledge, that is, knowledge that relates the characteristics of datasets with the performance of the available algorithms. This chapter describes how a simple metalearning system can be developed to generate metaknowledge that can be used to make recommendations concerning which algorithm to use on a given dataset. More details about various options are described in the next chapter. As there are many alternative algorithms for a given task (for instance, decision trees, neural networks and support vector machines can be used for classification), the approach of trying out all alternatives and choosing the best one becomes infeasible. Although, normally, only a limited number of existing methods are available for use in a given application, the number of these methods may still be too large to rule out extensive experimentation. An approach followed by many users is to make some preselection of a small number of alternatives based on knowledge about the data and the available methods. The methods are applied to the dataset and the best one is nor-mally chosen taking into account the results obtained. Although feasible, this approach may still require considerable computing time. Additionally, it re-quires that a highly skilled expert preselect the alternatives, and even the most skilled expert may sometimes fail, and so the best option may be left out.