Fuzzy classification with an artificial chemical process

In this work, a new algorithm to extract a compact set of if/then rules from data for classification problems is presented. The premise is extracted directly using LARES as a learning tool, which is a new global optimization procedure based on a new recently introduced paradigm called artificial chemical process. The conclusion part is determined using soft computing techniques. In the learning phase, the objective function minimizes the number of misclassified patterns from training data and reduces the conflicts between the rules to generate the pattern partition. The proposed method has many potential applications in industrial processes. Several examples are presented, including fault detection and operation of reactions with unstable regimes.