Communications of the ACM
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Robust trainability of single neurons
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Inductive logic programming and learnability
ACM SIGART Bulletin
The nature of statistical learning theory
The nature of statistical learning theory
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On the Power of Decision Lists
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Application of ILP to Problems in Chemistry and Biology (Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Combining Statistical and Relational Methods for Learning in Hypertext Domains
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Clique is hard to approximate within n1-
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Self-improved gaps almost everywhere for the agnostic approximation of monomials
Theoretical Computer Science
Hi-index | 5.23 |
Some authors have repeatedly pointed out that the use of the accuracy, in particular for comparing classifiers, is not adequate. The main argument concerns some assumptions of seldom validity or correctness underlying the use of this criterion. In this paper, we study the computational burden of the accuracy's replacement for building and comparing classifiers, using the framework of Inductive Logic Programming. Replacement is investigated in three ways: completion of the accuracy with an additional requirement, replacement of the accuracy with a bi-criterion recently introduced from statistical decision theory: the Receiver Operating Characteristic analysis, and replacement of the accuracy by a single criterion. We prove very hard results for most of the possible replacements. A first result shows that allowing the arbitrary multiplication of clauses appears to be totally useless. "Arbitrary" is to be taken in its broadest meaning, in particular exponential. The second point is the sudden appearance of the negative result, which is not a function of the criteria's demands. The third point is the equivalence in difficulty of all these different criteria. In contrast, the single accuracy's optimization appears to be tractable in this framework.