Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de Economía y CompetitividadXunta de Galici

Enhancing multi-class classification in FARC-HD fuzzy classifier: on the synergy between n-dimensional overlap functions and decomposition strategies

There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper we aim to improve the behaviour of FARC-HD fuzzy classifier in multi-class classification problems using decomposition strategies, and more specifically One-vs-One (OVO) and One-vs-All (OVA) strategies. However, when these strategies are applied on FARC-HD a problem emerges due to the low confidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product t-norm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of the fuzzy rules. As a result, robust aggregation strategies in OVO such as the weighted voting obtain poor results with this fuzzy classifier. In order to solve these problems, we propose to adapt the inference system of FARC-HD replacing the product t-norm with overlap functions. To do so, we define n-dimensional overlap functions. The usage of these new functions allows one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes. Furthermore, we propose a new aggregation strategy for OVO to deal with the problem of the weighted voting derived from the inappropriate confidences provided by FARC-HD for this aggregation method. The quality of our new approach is analyzed using twenty datasets and the conclusions are supported by a proper statistical analysis. In order to check the usefulness of our proposal, we carry out a comparison against some of the state-of-the-art fuzzy classifiers. Experimental results show the competitiveness of our method.This work was supported in part by the Spanish Ministry of Science and Technology under projects TIN2011-28488, TIN-2012-33856 and TIN-2013- 40765-P and the Andalusian Research Plan P10-TIC-6858 and P11-TIC-7765

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Measuring toxicity is an important step in drug development. However, the current experimental meth- ods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver