Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Information Sciences: an International Journal
A measure of variance for hierarchical nominal attributes
Information Sciences: an International Journal
A transitivity analysis of bipartite rankings in pairwise multi-class classification
Information Sciences: an International Journal
On the ERA ranking representability of pairwise bipartite ranking functions
Artificial Intelligence
| Hi-index | 0.07 |
Several techniques exist nowadays for continuous (i.e. numerical) data analysis and modeling. However, although part of the information gathered by companies, statistical offices and other institutions is numerical, a large part of it is represented using categorical variables in ordinal or nominal scales. Techniques for model building on categorical data are required to take advantage of such a wealth of information. In this paper, current approaches to regression for ordinal data are reviewed and a new proposal is described which has the advantage of not assuming any latent continuous variable underlying the dependent ordinal variable. Estimation in the new approach can be implemented using genetic algorithms. An artificial example is presented to illustrate the feasibility of the proposal.