The effect of numeric features on the scalability of inductive learning programs

The behaviour of a learning program as the quantity of data increases affects to a large extent its applicability on real-world problems. This paper presents the results of a theoretical and experimental investigation of the scalability of four well-known empirical concept learning programs. In particular it examines the effect of using numeric features in the training set. The theoretical part of the work involved a detailed worst-case computational complexity analysis of the algorithms. The results of the analysis deviate substantially from previously reported estimates, which have mainly examined discrete and finite feature spaces. In order to test these results, a set of experiments was carried out, involving one artificial and two real data sets. The artificial data set introduces a near-worst-case situation for the examined algorithms, while the real data sets provide an indication of their average-case behaviour.