Optimized Fuzzy Classification Using Genetic Algorithm

Kim, Myung Won; Ryu, Joung Woo

doi:10.1007/11539506_51

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Moreover, our model beats methods of Ishibuchi [8] on Sonar (20.67% vs. [22] on Sonar (20.67% vs. 28.37% and 30.29%); Wang weight refinement [17] on Sonar (20.67% vs. 29%), Iris (3.3% vs. 7%) and Pima (24.6% vs. 31%); Verikas [16] [11] on Iris (3.3% vs. 2%). It is worth to consider that several methods like [11] and [24] use GA approach to improve performance of fuzzy rules system whereas here we only use traditional approach for fuzzy rule with some limitation on number of attributes and membership function to generate rule.…”

Section: Resultsmentioning

confidence: 99%

“…Ishibuchi [10,11] improved performance of fuzzy classification system based on rules by applying Genetic Algorithm (GA) approach with single, two and three-objective. By the same approach, Kim [24] also proposed new fitness function for GA and several new XOR membership functions. On the other hand, Verikas [16] limited classifier structure by using SOM tree algorithm and then applied GA to specify input variables for each fuzzy rule.…”

Section: Step4 (Classification Based On Rule Set)mentioning

confidence: 99%

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Fuzzy If-Then Rules Classifier on Ensemble Data

Nguyen

Liew

et al. 2014

Communications in Computer and Information Science

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This paper introduces a novel framework in which classification method based on fuzzy IF-THEN rules is cooperated with an ensemble system. This model tackles several drawbacks. First, IF-THEN rules approaches have problem with high dimensional data since computational cost is exponential function. In this framework, rules are operated on outputs of base classifiers which have frequently lower dimension than original data. Moreover, outputs of base classifiers are scaled within the range [0, 1] so it is convenient to apply fuzzy rules directly instead of data transformation and normalization as requirement for inputs of fuzzy rules. The performance of this model was evaluated through experiments on 6 popular datasets from UCI Machine Learning Repository and comparisons with other state-of-art combining classifiers algorithms as well as other fuzzy IF-THEN rules approaches. The results show that our framework can improve the classification accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Step4 (Classification Based On Rule Set)mentioning

confidence: 99%

Fuzzy If-Then Rules Classifier on Ensemble Data

Nguyen

Liew

et al. 2014

Communications in Computer and Information Science

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“…Abonyi and Szeifert [9] achieved a classification success rate of 95.57% using the controlled fuzzy set method. A classification success rate of 96.66% was achieved in the study by Kim et al [10] using a fuzzy rule-based method. Sahan et al [11] achieved a success rate of 99.14% using a hybrid model based on fuzzy artificial immunity and K-nearest neighbor in their studies.…”

Section: Studies For the Diagnosis Of Bcmentioning

confidence: 97%

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya¹

2013

Turk J Elec Eng & Comp Sci

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Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.

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“…The rules of this kind of system are frequently characterized as “IF…THEN” statement and each rule can be defined as a fuzzy conception. Fuzzy rules permit to successfully categorize data having non-axis-parallel decision limits, which is difficult for conventional attribute-based methods [41].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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Optimized Fuzzy Classification Using Genetic Algorithm

Cited by 8 publications

References 4 publications

Fuzzy If-Then Rules Classifier on Ensemble Data

Fuzzy If-Then Rules Classifier on Ensemble Data

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

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