%0 Conference Paper %1 Clark89thecn2 %A Clark, Peter %A Niblett, Tim %B Machine Learning %D 1989 %K algorithm cn2 learning machine %P 261--283 %T The CN2 Induction Algorithm %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9180 %X Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the cn2, id3 and aq algorithms are compared on three medical classification tasks. Keywords: concept learning, rule induction, noise, comprehensibility, cn2. 1 Introduction In the task of constructing expert systems, systems for inducing concept descriptions from examples have proved useful in easing the bottleneck of knowledge acquisition 1. Two families of systems, based on the id3 2 and aq 3 algorithms, have been especially successful. These basic algorithms assume no noise in the domain, searching for a concept description that classifies training data perfectl...

@inproceedings{Clark89thecn2, abstract = {Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the cn2, id3 and aq algorithms are compared on three medical classification tasks. Keywords: concept learning, rule induction, noise, comprehensibility, cn2. 1 Introduction In the task of constructing expert systems, systems for inducing concept descriptions from examples have proved useful in easing the bottleneck of knowledge acquisition [1]. Two families of systems, based on the id3 [2] and aq [3] algorithms, have been especially successful. These basic algorithms assume no noise in the domain, searching for a concept description that classifies training data perfectl...}, added-at = {2011-08-03T09:39:59.000+0200}, author = {Clark, Peter and Niblett, Tim}, biburl = {https://www.bibsonomy.org/bibtex/2bfa5feec075f7b47142e40acd9927063/poeschko}, booktitle = {Machine Learning}, description = {CiteSeerX — The CN2 Induction Algorithm}, interhash = {25033db7a243d1cf6f0347e0f93f7c1c}, intrahash = {bfa5feec075f7b47142e40acd9927063}, keywords = {algorithm cn2 learning machine}, pages = {261--283}, timestamp = {2011-08-03T09:39:59.000+0200}, title = {The CN2 Induction Algorithm}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9180}, year = 1989 }