An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

AbstractNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability

Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

AbstractIn this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results

An efficient multi-objective evolutionary fuzzy system for regression problems

During the last years, multi-objective evolutionary algorithms (MOEAs) have been extensively employed as optimization tools for generating fuzzy rule-based systems (FRBSs) with different trade-offs between accuracy and interpretability from data. Since the size of the search space and the computational cost of the fitness evaluation depend on the number of input variables and instances, respectively, managing high-dimensional and large datasets is a critical issue. In this paper, we focus on MOEAs applied to learn concurrently the rule base and the data base of Mamdani FRBSs and propose to tackle the issue by exploiting the synergy between two different techniques. The first technique is based on a novel method which reduces the search space by learning rules not from scratch, but rather from a heuristically generated rule base. The second technique performs an instance selection by exploiting a co-evolutionary approach where cyclically a genetic algorithm evolves a reduced training set which is used in the evolution of the MOEA. The effectiveness of the synergy has been tested on twelve datasets. Using non-parametric statistical tests we show that, although achieving statistically equivalent solutions, the adoption of this synergy allows saving up to 97.38% of the execution time with respect to a state-of-the-art multi-objective evolutionary approach which learns rules from scratch

Exergame e dispositivi wearable per la didattica esercitativa nei corsi di laurea on line in scienze delle attività motorie e sportive

. In questo articolo vengono presentati alcuni scenari di utilizzo di controller per exergame e sensori wearable come soluzioni BYOD per le attività di didattica esercitativa a distanza nei corsi di laurea on line in scienze delle attività motorie e sportive. In particolare, tali dispositivi vengono inquadrati come sorgenti di variabili cinematiche con cui poter monitorare e valutare a distanza l’esecuzione di determinate esercitazioni motorie e sportive fornendo un feedback allo studente. Viene presentata, inoltre, l’integrazione di tali dispositivi in una possibile architettura di comunicazione complessiva di una piattaforma di e-Learning di una università telematica basata sul Cloud e sul Fog Computing. Ciò permetterebbe la realizzazione di una didattica personalizzata e interattiva a distanza che faciliterebbe le esperienze di apprendimento ubiquo e context-aware in situazioni informali, stimolando altresì gli studenti all’apprendimento

Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-Based Systems

In the last years, the numerous successful applications of Mamdani Fuzzy Rule-Based Systems (MFRBSs) to several different domains have produced a considerable interest in methods to generate MFRBSs from data. Most of the methods proposed in the literature, however, focus on performance maximization and omit to consider MFRBS comprehensibility. Only recently, the problem of finding the right trade-off between performance and interpretability, in spite of the original nature of fuzzy logic, has arisen a growing interest in methods which take both the aspects into account. In this Ph.D. thesis, we propose the use of multi-objective evolutionary algorithms to design the structure of MFRBSs with good trade-off between accuracy and complexity. Complexity is always measured as sum of the conditions which compose the antecedents of the rules included in the MFRBS while the accuracy depends on the specific application of the system. As regards the application to regression problems, we first introduce a variant of the well-known (2+2) Pareto Archived Evolutionary Strategy ((2+2)PAES), which adopts the one-point crossover and two appropriately defined mutation operators, to generate a set of non-dominated rule bases (RBs) in the error-complexity space. Then, we extend this approach to learn concurrently both the RB and the data base (DB) in the multi-objective evolutionary framework. In particular, we introduce two approaches that allow to learn concurrently the RB and the partition granularity or the membership function parameters, respectively. In the first case, we introduce the concept of virtual RB, in order to handle RBs defined on different variable partitions, while in the latter case we exploit the linguistic 2-tuple representation for the fuzzy sets, which allows the symbolic translation of a linguistic label by only considering one parameter. Regarding classification with imbalanced datasets, we exploit a three-objective evolutionary algorithm, namely NSGA-II, to generate a set of RBs for Fuzzy Rule Based Classifiers with different trade-offs among sensitivity, specificity and complexity. Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Intensive experimentations have been performed and the results obtained with the proposed approaches and with comparison techniques have been extensively discussed

Evolutionary fuzzy classifiers for imbalanced datasets: An experimental comparison2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

In this paper, we compare three state-of-the-art evolutionary fuzzy classifiers (EFCs) for imbalanced datasets. The first EFC performs an evolutionary data base learning with an embedded rule base generation. The second EFC builds a hierarchical fuzzy rule-based classifier (FRBC): first, a genetic programming algorithm is used to learn the rule base and then a post-process, which includes a genetic rule selection and a membership function parameters tuning, is applied to the generated FRBC. The third EFC is an extension of a multi-objective evolutionary learning scheme we have recently proposed: the rule base and the membership function parameters of a set of FRBCs are concurrently learned by optimizing the sensitivity, the specificity and the complexity. By performing non-parametric statistical tests, we show that, without re-balancing the training set, the third EFC outperforms, in terms of area under the ROC curve, the other comparison approaches

Successful treatment of Fusarium keratitis after photo refractive keratectomy.

A 39-year-old woman presented to our hospital with a history of photorefractive keratectomy (PRK), performed two weeks prior; slit-lamp examination revealed diffuse conjunctival congestion, corneal ulcer and stromal infiltration. After 5 days of antifungal and antibacteric treatment, the infiltrate progressively increased so that a therapeutic penetrating keratoplasty was necessary. The microbiological analyses revealed the presence of fungal filaments. Twenty days after surgery the patient had recurrent fungal infiltrate in the donor cornea with wound dehiscence. We performed a second penetrating keratoplasty. With the matrix-assisted-laser-desorption-ionization-time-of-flight analysis (MALDI-TOF) we identified a Fusarium solani. Intravenous amphothericine B, a combination of intracameral and intrastromal voriconazole and intracameral amphotericine B were administered. After 6 months from the last surgery the infection was eradicated. The management of fungal keratitis after PRK depends on many factors: In our experience, a prompt keratoplasty and the use of intracameral antifungal medication proved to be very effective