%0 Journal Article %1 Reiser:2001:AI %A Reiser, Philip G. K. %A Riddle, Patricia J. %D 2001 %J Applied Intelligence %K ILP algorithms, evolutionary genetic inductive learning, logic machine programming, sampling, %N 3 %P 181--197 %R doi:10.1023/A:1011239013893 %T Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach %U http://www.stancomb.co.uk/~prr/Papers/AppInt.ps %V 15 %X Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>10000 examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs. This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms. The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.

@article{Reiser:2001:AI, abstract = {Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>10000 examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs. This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms. The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Reiser, Philip G. K. and Riddle, Patricia J.}, biburl = {https://www.bibsonomy.org/bibtex/2dc6d1615bf289b160eb06fc939a6e3f3/brazovayeye}, doi = {doi:10.1023/A:1011239013893}, interhash = {013afb227c7434aa3898e4d128732ab5}, intrahash = {dc6d1615bf289b160eb06fc939a6e3f3}, issn = {0924-669X}, journal = {Applied Intelligence}, keywords = {ILP algorithms, evolutionary genetic inductive learning, logic machine programming, sampling,}, month = {November-December}, note = {Special Issue: Simulated Evolution and Learning}, notes = {Article ID: 354285 Crossover}, number = 3, pages = {181--197}, size = {17 pages}, timestamp = {2008-06-19T17:50:11.000+0200}, title = {Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach}, url = {http://www.stancomb.co.uk/~prr/Papers/AppInt.ps}, volume = 15, year = 2001 }