S-FRULER: Scalable fuzzy rule learning through evolution for regression

Rodríguez-Fdez, Ismael; Mucientes, Manuel; Bugarín, Alberto

doi:10.1016/j.knosys.2016.07.034

Cited by 21 publications

(15 citation statements)

References 29 publications

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“…Most commonly, a model structure in the form of an FRBS is considered. These systems have displayed their superiority to classical decision trees and crisp rule-based systems and their robustness in different AI applications including classification [2,6,25], big data analysis [6,27], and regression [19,26]. FRBSs allow for the definition of simple and verbally formulated rules over imprecise domains which can be combined to generate precise yet understandable results.…”

Section: Rule-based Systemmentioning

confidence: 99%

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Tsakiridis

Diamantopoulos

Symeonidis

et al. 2020

IFIP Advances in Information and Communication Technology

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The increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations.

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Section: Rule-based Systemmentioning

confidence: 99%

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Tsakiridis

Diamantopoulos

Symeonidis

et al. 2020

IFIP Advances in Information and Communication Technology

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“…Hence, Jin's 105 algorithm generates 27 rules from a training set of 20000 examples described by 11 inputs. In [33], authors present S-FRULER a genetic fuzzy system for regression problem capable of learning rules from big datasets. First, a multigranularity fuzzy discretization is applied on the whole dataset, which is then split into several partitions.…”

Section: Related Workmentioning

confidence: 99%

A fuzzy expert system architecture for data and event stream processing

Poli

Boudet

2018

Fuzzy Sets and Systems

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The Internet of Things was born from the proliferation of connected objects and is known as the third era of information technology. It results in the availability of a huge amount of continuously acquired data which need to be processed to be more valuable. This leads to a real paradigm shift: instead of processing fixed data like classical databases or files, the new algorithms have to deal with

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“…Hence, it is possible to adapt not only the granularity but also the disposition of the functions. This sort of approach is a typical example of a 2‐tuple approach (Herrera & Martinez, ), which has often been used in recent literature (Alcalá, Gacto, & Herrera, ; Gacto, Alcalá, & Herrera, ; Palacios, Palacios, Sánchez, & Alcalá‐Fdez, ; Rodríguez‐Fdez, Mucientes, & Bugarín, ; Rodríguez‐Fdez, Mucientes, & Bugarín, ). Rule Base Learning : after the definition of the Parameter Base, an EA is responsible for extracting rules from a dataset. There are many approaches to codify a set of rules; in general, they tend to concentrate exclusively on the learning process of a rule base (Herrera, ).…”

Section: Evolutionary Fuzzy Systemsmentioning

confidence: 99%

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Koshiyama

Tanscheit

Vellasco

2018

WIREs Data Min & Knowl

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Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning

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S-FRULER: Scalable fuzzy rule learning through evolution for regression

Cited by 21 publications

References 29 publications

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

A fuzzy expert system architecture for data and event stream processing

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

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