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 positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Induction of accurate and interpretable fuzzy rules from preliminary crisp representation

This paper proposes a novel approach for building transparent knowledge-based systems by generating accurate and interpretable fuzzy rules. The learning mechanism reported here induces fuzzy rules via making use of only predefined fuzzy labels that reflect prescribed notations and domain expertise, thereby ensuring transparency in the knowledge model adopted for problem solving. It works by mapping every coarsely learned crisp production rule in the knowledge base onto a set of potentially useful fuzzy rules, which serves as an initial step towards an intuitive technique for similarity-based rule generalisation. This is followed by a procedure that locally selects a compact subset of the emerging fuzzy rules, so that the resulting subset collectively generalises the underlying original crisp rule. The outcome of this local procedure forms the input to a global genetic search process, which seeks for a trade-off between accuracy and complexity of the eventually induced fuzzy rule base while maintaining transparency. Systematic experimental results are provided to demonstrate that the induced fuzzy knowledge base is of high performance and interpretabilitypublishersversionPeer reviewe

A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING

During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results

Evolutionary Learning of Fuzzy Rules for Regression

The objective of this PhD Thesis is to design Genetic Fuzzy Systems (GFS) that learn Fuzzy Rule Based Systems to solve regression problems in a general manner. Particularly, the aim is to obtain models with low complexity while maintaining high precision without using expert-knowledge about the problem to be solved. This means that the GFSs have to work with raw data, that is, without any preprocessing that help the learning process to solve a particular problem. This is of particular interest, when no knowledge about the input data is available or for a first approximation to the problem. Moreover, within this objective, GFSs have to cope with large scale problems, thus the algorithms have to scale with the data

Granular computing approach for intelligent classifier design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its efficiency. Fuzzy inference systems, neural networks, neuro-fuzzy systems and classifier ensembles are considered to evaluate the efficiency of the proposed approach. Each of the considered systems is designed using the proposed approach and classification performance is evaluated and compared to that of the standard system. The proposed approach is based on constructing information granules from data at multiple levels of granularity. The granulation process is performed using a modified fuzzy c-means algorithm that takes classification problem into account. Clustering is followed by a coarsening process that involves merging small clusters into large ones to form a lower granularity level. The resulted granules are used to build each of the considered binary classifiers in different settings and approaches. Granules produced by the proposed granulation method are used to build a fuzzy classifier for each granulation level or set of levels. The performance of the classifiers is evaluated using real life data sets and measured by two classification performance measures: accuracy and area under receiver operating characteristic curve. Experimental results show that fuzzy systems constructed using the proposed method achieved better classification performance. In addition, the proposed approach is used for the design of neural network classifiers. Resulted granules from one or more granulation levels are used to train the classifiers at different levels of specificity/abstraction. Using this approach, the classification problem is broken down into the modelling of classification rules represented by the information granules resulting in more interpretable system. Experimental results show that neural network classifiers trained using the proposed approach have better classification performance for most of the data sets. In a similar manner, the proposed approach is used for the training of neuro-fuzzy systems resulting in similar improvement in classification performance. Lastly, neural networks built using the proposed approach are used to construct a classifier ensemble. Information granules are used to generate and train the base classifiers. The final ensemble output is produced by a weighted sum combiner. Based on the experimental results, the proposed approach has improved the classification performance of the base classifiers for most of the data sets. Furthermore, a genetic algorithm is used to determine the combiner weights automatically.Higher Committee for Education Development in Iraq (HCED