Abstract - In this paper, we analyze and compare four multi-objective evolutionary granular rule-based classifiers. We learn concurrently the rule base, the most suitable number of granules and their parameters during the evolutionary process. Rule learning is performed by a method, which selects rules and conditions from an initial heuristically-generated rule base. The four classifiers differ for the type of granule, namely Type-1 and Type-2 fuzzy sets, and for the method used for generating the initial rule base, namely crisp and fuzzy decision tree learning algorithms. Results show that generating the initial rule base by using a fuzzy decision tree outperforms the use of the crisp decision tree. On the other hand, no statistical difference exists between the use of Type-1 and Type-2 fuzzy sets as granules.
Abstract - In this paper, we present DECO3RUM (Differential Evolution based Cooperative and Competing learning of Compact Rule-based Models), an evolutionary Mamdani Fuzzy Rule-based System for modeling problems. DECO3RUM follows the Genetic Cooperative Competitive Learning approach, and utilizes the Differential Evolution algorithm as its learning algorithm. A real world high dimensional dataset from the domain of soil science was considered to evaluate the ability of DECO3RUM to handle Big Data problems, where the number of features is significant. DECO3RUM was shown to statistically out-perform the most prevailing methodology used in soil spectroscopy, namely the Partial Least Squares Regression algorithm.
Abstract - Internet traffic classification has moved in the last years from traditional port and payload--based approaches towards methods employing statistical measurements and machine learning techniques. Despite the success achieved by these techniques, they are not able to explain the relation between the features, which describe the traffic flow, and the corresponding traffic classes. This relation can be extremely useful to network managers for quickly handling possible network drawback. In this paper, we propose to tackle the traffic classification problem by using multi-objective evolutionary fuzzy classifiers (MOEFCs). MOEFCs are characterised by good trade-offs between accuracy and interpretability. We adopt two Internet traffic datasets extracted from two real-world networks. We discuss the results obtained both by applying a cross validation on each single dataset, and by using a dataset as training set and the other as test set. We show that, in both cases, MOEFCs can achieve satisfactory accuracy in the face of low complexity and, therefore, high interpretability.
Abstract - Various evolutionary multiobjective optimization (EMO) algorithms have been used in the field of evolutionary fuzzy systems (EFS), because EMO algorithms can easily handle multiple objective functions such as the accuracy maximization and complexity minimization for fuzzy system design. Most EMO algorithms used in EFS are Pareto dominance-based algorithms such as NSGA-II, SPEA2, and PAES. There are a few studies where other types of EMO algorithms are used in EFS. In this paper, we apply a multiobjective evolutionary algorithm based on decomposition called MOEA/D to EFS for fuzzy classifier design. MOEA/D is one of the most well-known decomposition-based EMO algorithms. The key idea is to divide a multiobjective optimization problem into a number of single-objective problems using a set of uniformly distributed weight vectors in a scalarizing function. We propose a new scalarizing function called an accuracy-oriented function (AOF) which is specialized for classifier design. We examine the effects of using AOF in MOEA/D on the search ability of our multiobjective fuzzy genetics-based machine learning (GBML). We also examine the synergy effect of MOEA/D with AOF and parallel distributed implementation of fuzzy GBML on the generalization ability.