An algorithm which learns multiple covers via integer linear programming. Part II: experimental results and conclusions

Abstract

Presents an inductive machine learning algorithm, CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. The complexity of the algorithm is analysed and the results are compared with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. Published in two parts: I – The CLILP2 algorithm; II – Experimental results and conclusions.

Citation

Cios, K.J. and Liu, N. (1995), "An algorithm which learns multiple covers via integer linear programming. Part II: experimental results and conclusions", Kybernetes, Vol. 24 No. 3, pp. 24-36. https://doi.org/10.1108/03684929510147029