Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the Definition and Representation of a Ranking
ReIMICS '01 Revised Papers from the 6th International Conference and 1st Workshop of COST Action 274 TARSKI on Relational Methods in Computer Science
Sparse episode identification in environmental datasets: The case of air quality assessment
Expert Systems with Applications: An International Journal
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
Hi-index | 12.05 |
Many expert systems solve classification problems. While comparing the accuracy of such classifiers, the cost of error must frequently be taken into account. In such cost-sensitive applications just using the percentage of misses as the sole meter for accuracy can be misleading. Typical examples of such problems are medical and military applications, as well as data sets with ordinal (i.e., ordered) class. A new methodology is proposed here for assessing classifiers accuracy. The approach taken is based on Cohen's Kappa statistic. It compensates for classifications that may be due to chance. The use of Kappa is proposed as a standard meter for measuring the accuracy of all multi-valued classification problems. The use of Weighted Kappa enables to effectively deal with cost-sensitive classification. When the cost of error is unknown and can only be roughly estimated, the use of sensitivity analysis with Weighted Kappa is highly recommended.