A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base

Cordón, Óscar; Herrera, Francisco; Magdalena, Luis; Villar, Pedro

doi:10.1016/s0020-0255(01)00143-8

Cited by 98 publications

(76 citation statements)

References 22 publications

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“…As the results did not show any relevant difference in terms of accuracy, five strongly partitioned fuzzy sets ( Figure 3) have been used throughout the experiments, as stated in Table 3. In addition to FS-MOGFS+TUN [3], three other SFGs have been used for comparison: GR-MF [22],GA-WM [23] and GLD-WM [19].…”

Section: In Its Basic Version Fs-mogfs Consists Ofmentioning

confidence: 99%

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Koshiyama¹,

Vellasco²,

Tanscheit³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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This work presents a novel Genetic Fuzzy System (GFS), called Genetic Programming Fuzzy Inference System for Regression problems (GPFISRegress). It makes use of Multi-Gene Genetic Programming to build the premises of fuzzy rules, including t-norms, negation and linguistic hedge operators. GPFIS-Regress also defines a consequent term that is more compatible with a given premise and makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. The system has been applied to a set of benchmarks and has also been compared to other GFSs, showing competitive results in terms of accuracy and interpretability.

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Section: In Its Basic Version Fs-mogfs Consists Ofmentioning

confidence: 99%

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Koshiyama¹,

Vellasco²,

Tanscheit³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…• Embedded design derives the DB using an embedded basic learning method (Cordón, Herrera, Magdalena & Villar, 2001;Glorennec, 1996;Ishibuchi & Murata, 1996).…”

Section: Fuzzy Partition Designmentioning

confidence: 99%

Expert guided integration of induced knowledge into a fuzzy knowledge base

Guillaume¹,

Magdalena

2006

Soft Comput

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This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria: range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.

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“…A DB generation process wraps an RB learning one working as follows: each time a DB has been obtained by the DB definition process, the RB generation method is used to derive the rules, and some type of error measure is used to validate the whole KB obtained. The works proposed in [6,7,8] use Simulated Annealing and GAs to learn an appropiate DB in a Fuzzy Rule-Based System. The method proposed in [14] considers a GA to design an FRBCS, working in the said way.…”

Section: Db Learningmentioning

confidence: 99%

A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems

Cordón¹,

Herrera²,

Jesus³

et al. 2003

Intelligent Systems for Information Processing

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In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition. An ad-hoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good balance between accuracy and interpretability.

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A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base

Cited by 98 publications

References 22 publications

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Expert guided integration of induced knowledge into a fuzzy knowledge base

A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems

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