An Incremental Approach to Address Big Data Classification Problems Using Cognitive Models

Abstract

The recent emergence of massive amounts of data requires new algorithms that are capable of processing them in an acceptable time frame. Several proposals have been made, and all of them share the idea of using a procedure to break down the entire set of examples into smaller subsets, process each subset with a learning algorithm, and then combine the different partial results. Most of these models make use of a parallel process, where each learning algorithm learns independently for each subset of data. In our case, the goal is to propose a new model to obtain classifiers based on fuzzy rules that make use of a sequential model that can process a large number of examples and to show that, for some problems, a sequential procedure can be competitive in time and learning capacity against parallel processing proposals based on the MapReduce paradigm. This sequential processing uses a batch-incremental learning technique that can process each subset of examples. The incremental proposal makes use of a biologically inspired computation method. This method is a cognitive computational model which uses genetic algorithms to learn fuzzy rules. The experimentation carried out shows that the incremental model is competitive with respect to a parallel model proposed for addressing big data classification using fuzzy rules.