Ensembles of Classifiers, Choquet Integral, Pre-Aggregation Functions, Overlap Functions, Quasi-Overlap Funcions, Validation Indexes
Ensembles of classifiers is an method in machine learning that consists in a collection of classifiers that process the same information and their output is combined in some manner. The process of classification is done in two main steps: the classification step and the combination step. In the classification step, each classifier processes the information and provides an output. In the combination step, the output of every classifier is combined, providing a single output. Although the combination step is extremely important, most works focus mostly on the classification step. Therefore, in this work, generalizations of the Choquet INtegral will be proposed to be used as a combination method in ensembles of classifiers. The main idea is to allow a greater freedom of choice for functions in the integral, opening possibilities for otimization and using functions adequate to the data. Furthermore, a new class of aggregation functions will be proposed to be used in conjunction with this method. Preliminary results show that this model was capable of obtaining good results, performing better than known methods like AdaBoost and Bagging. In addition, the integrals that involved the proposed aggregation functions had good performance.