Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

Galende, Marta; Sainz-Palmero, G.I.; Fuente, José Luis de la

doi:10.1007/s00500-011-0748-6

Cited by 19 publications

(4 citation statements)

References 66 publications

(99 reference statements)

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“…Dalam teknik ini, tidak ada solusi optimal, ideal dan tunggal yang dapat diturunkan, sebaliknya, serangkaian solusi dihasilkan karena peningkatan dalam satu tujuan menyebabkan degradasi pada tujuan yang tersisa. Solusi ini dikenal dengan istilah Solusi Pareto-Optimal [7].…”

Section: Journal Shift Vol 2 No 1 (2022)unclassified

Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier

Salman,

Mustikasari,

Akbar

2022

shift

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Classification is one of the key issues in the field of data mining and knowledge discovery. This paper implements a method of constructing a fuzzy rule mining classifier, which is extended in the context of classification. There are three stages of this approach: fuzzy rule set extraction, second; a linguistic labeling process that assigns a linguistic label to each fuzzy set. Owing to many attributes in the database, the feature selection process is also carried out, reducing the complexity to build the final classifier. Third: incorporate strategies to avoid rule redundancy and conflict into process mining. We applied the application Multiobjective Evolutionary Fuzzy Classifier (MOFC), which produced a classifier with satisfactory classification accuracy compared to other classifiers such as C4.5. In addition, in terms of classification based on association rules, MOFC can filter the large of rules and be proven to be able to build compact fuzzy models while maintaining a very good level of accuracy and producing a much smaller set of rules. We examine the performance of fuzzy rule classifiers through computational experiments on three benchmark data sets in the UCI machine learning repository.

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Section: Journal Shift Vol 2 No 1 (2022)unclassified

Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier

Salman,

Mustikasari,

Akbar

2022

shift

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“…The CMF issue (see Fig. 6) can be resolved by the following means: 1) reducing MFs (7) (Lee et al, 2011;Akbarzadeh-T et al, 2000), 2) optimization ( 8) (Akbarzadeh-T et al, 2000), 3) neural network (2) (Lee et al, 2011;Galende-Hernández et al, 2012), etc. The data complexity is reduced using genetic algorithms (1), other evolutionary algorithms (4), fuzzy rule reduction (6), approximation techniques (16) (for explanations, see below in this section) and decision table/tree (9) (Antonelli et al, 2016).…”

Section: Found Solutions For Complexity Issues (Rq2)mentioning

confidence: 99%

A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

Kalibatiene¹,

Miliauskaitė²

2021

Informatica

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The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

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“…The accuracy and interpretability measures considered in this work are defined in [21]. The accuracy of the model is measured through its Mean Squared Error (MSE) (Eq.…”

Section: B Accuracy and Interpretability Measuresmentioning

confidence: 99%

Checking orthogonal transformations and genetic algorithms for selection of fuzzy rules based on interpretability-accuracy concepts

Rey¹,

Galende

Sainz

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Fuzzy modeling is one of the most known and used techniques in different areas to emulate the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, the models can present a poor performance.Several approaches are found in the specialized literature to reduce the complexity and improve the interpretability of the fuzzy models. Here, a post-processing approach is taken into account via the definition of the rules selection criterion that aims to choose the most relevant rules according to the wellknown accuracy-interpretability trade-off. This criterion is based on Orthogonal Transformations, here the QRP transformation is taking into consideration, and its parameters are tuned genetically. The main objective is to check the true significance, drawbacks and advantages the firing matrix of the rules, that is the foundation of the most usual approaches based on orthogonal transformations for the complexity reduction of the fuzzy models.A neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this approach. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), each with its own particularities and complexities from the point of view of fuzzy sets and rule generation. NSGA-II is the MOEA tool used to tune the criterion parameters based on accuracy-interpretability ideas.

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Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

Cited by 19 publications

References 66 publications

Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier

Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier

A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

Checking orthogonal transformations and genetic algorithms for selection of fuzzy rules based on interpretability-accuracy concepts

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