Knowledge extraction using a genetic fuzzy rule-based system with increased interpretability

Ishibashi, Rogério; Nascimento, Cairo Lúcio

doi:10.1109/sami.2012.6208967

Cited by 8 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A GFRBS (Genetic Fuzzy Rule Based System) assisted by a Decision Tree was presented by the authors in two previous works in [11] and [12]. In both works, a GFRBS with a single objective optimization was used with the following three steps:…”

Section: Mogft-imentioning

confidence: 99%

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

Ishibashi

Nascimento

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

Self Cite

View full text Add to dashboard Cite

In this article a set of well-known computational intelligence techniques such as Decision Trees, Fuzzy Logic, and Multi-objective Optimization Genetic Algorithm are combined to generate a novel hybrid method which is called MoGFT-I: Multi-objective Genetic Fuzzy Rule Based System supported by a Decision Tree with Improved Interpretability. The output of the proposed supervised learning method is a set of Mamdani-type fuzzy systems which are optimized and distributed along a Pareto curve by considering two conflicting attributes: accuracy and interpretability. The MoGFT-I method is then applied to the Cart and Pole control problem such that a set of feedback controller are designed to control this unstable nonlinear dynamical system.

show abstract

Section: Mogft-imentioning

confidence: 99%

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

Ishibashi

Nascimento

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…If the temperature of an object is 10 (in a certain temperature scale), in the classical view this temperature receives the label "low temperature" since the degree of pertinence of "temperature = 10" is 1 to the classical set of "low temperatures". In the fuzzy view, 'temperature = 10" is simultaneously "low" and "medium" since the degrees of pertinence to the fuzzy sets "low temperature" and "medium temperature" are 0.2 and 0.5 respectively [1]. .…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…Crossover is the combination of the genetic material of any two individuals in the current generation to generate the individuals of the next generation. Figure 2 shows a possible crossover implementation [1].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…. Figure 1 shows an example of a decision tree that can be used to decide if one should go outside to play golf considering the weather conditions [1].…”

Section: Decision Treementioning

confidence: 99%

“…It creates a decision tree using any tree induction algorithm such as CART, ID3 or C4.5.The parameters of the membership functions are adjusted by the genetic algorithm. [1] In 2011 M.J. Gacto, R. Alcalá, F. Herrera proposed an overview of the proposed interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based system. It proposes a taxonomy based on a double axis Complexity versus semantic interpretability considering the two main kinds of measures; and ''rule base versus fuzzy partitions'' which considers the different components of the knowledge base to which both kinds of measures can be applied.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Knowledge Extraction Classification using Different Techniques

Shrivastava¹,

Singh²,

Shrivastava³

2013

IJCA

View full text Add to dashboard Cite

Knowledge extraction is the method of extracting some useful information from a set of databases such that the extracted information can be used in a wide variety of applications. Here a brief survey of different techniques of classification for the knowledge extraction is given. Although there are many technique used for the classification but here the knowledge extraction for useful information techniques is presented.

show abstract

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

View full text Add to dashboard Cite

The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

show abstract

Knowledge extraction using a genetic fuzzy rule-based system with increased interpretability

Cited by 8 publications

References 10 publications

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

A Survey of Knowledge Extraction Classification using Different Techniques

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

Contact Info

Product

Resources

About