Inverted Hierarchical Neuro-Fuzzy BSP System: a Novel Neuro-Fuzzy Model for Pattern Classification and Rule Extraction in Databases  

Oleh:

Goncalves, Laercio Brito ; Vellasco, Marley Maria Bernardes Rebuzzi ; Pacheco, Marco Aurelio Cavalcanti ; Souza, Flavio Joaquim de

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 36 no. 2 (Mar. 2006), page 236-248.
Topik: Binary Space Partitioning (BPS); Neuro-Fuzzy Systems; Pattern Classification; Rule Extraction

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.

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