MULTICLASS CLASSIFIER FROM A COMBINATION OF LOCAL EXPERTS: TOWARD DISTRIBUTED COMPUTATION FOR REAL-PROBLEM CLASSIFIERS
Christoph König, Giuseppina C. Gini, Marian Crăciun, Emilio Benfenati · International Journal of Pattern Recognition and Artificial Intelligence · 2004
In many real-world applications simple classifiers are too weak to have predictive power. Ensemble techniques, or mixture of experts, are a possible solution. We illustrate why mixture of experts are a natural choice in domains such as the prediction of environmental toxicity for chemicals, when a structural approach is pursued. The real data here used are derived from peer reviewed experiments, and are publicly available, but are difficult to model. We used them to predict aquatic toxicity for fish. Chemical information was coded into a set of about 160 descriptors; after reducing the dimensions of the feature vector through different techniques, we developed multivariate regression to build a model of the toxic effects of chemicals. Defining toxicity as a category, as in European Union (EU) regulations, we extended the study to predict toxicity class. Problems with poor predictive power of this simple approach have led us to reconsider the problem from a more theoretical angle. We have respected locality criterion to build different local classifiers, one for each chemical class, to achieve better results. Then we combined the classifiers to get a complete system to predict any chemical for the chemical classes studied.