The effect of numeric features on the scalability of inductive learning programs [chapter]

Georgios Paliouras, David S. Brée
1995 Lecture Notes in Computer Science  
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 anMysis of the algorithms. The
more » ... ults of the anMysis 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.
doi:10.1007/3-540-59286-5_60 fatcat:qupbcpq7qrh6tgenfabfmag43q