A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification

This paper focuses on the problem of making decisions in the context of nominal data under specific constraints. The underlying goal driving the methodology proposed here is to build a decision-making model capable of classifying as many samples as possible while avoiding false positives at all costs, all within the smallest possible computational time. Under such constraints, one of the best type of model is the cognitive-inspired extreme learning machine (ELM), for the final decision process. A two-stage decision methodology using two types of classifiers, a distance-based one, K-NN, and the cognitive-based one, ELM, provides a fast means of obtaining a classification decision on a sample, keeping false positives as low as possible while classifying as many samples as possible (high coverage). The methodology only has two parameters, which, respectively, set the precision of the distance approximation and the final trade-off between false-positive rate and coverage. Experimental results using a specific dataset provided by F-Secure Corporation show that this methodology provides a rapid decision on new samples, with a direct control over the false positives and thus on the decision capabilities of the model.