A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

Gamal, Mona; El-Fetouh, Ahmed Abou; Barakat, Shereef

doi:10.14569/ijacsa.2013.040501

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…where μ A represents the membership function of A and μ A x represents the degree of subordination of element x to set A, that is, membership [71,72].…”

Section: Influence Factormentioning

confidence: 99%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

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The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

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“…where μ A represents the membership function of A and μ A x represents the degree of subordination of element x to set A, that is, membership [71,72].…”

Section: Influence Factormentioning

confidence: 99%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

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“…Fuzzy-rough feature selection (FRFS) gives a method by which discrete or real-valued noisy data can be successfully eliminated without the requirement for user supplied data. Furthermore, this procedure can be implemented with nominal or continuous attributes that can be found in classification and regression datasets [27], [30]- [38].…”

Section: B) Fuzzy Rough Feature Selection (Frfs)mentioning

confidence: 99%

A Proposed Hybrid Effective Technique for Enhancing Classification Accuracy

El‐Hasnony¹,

El-Bakry²,

Al-Tarawneh³

et al. 2017

ijacsa

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Abstract-The automatic prediction and detection of breast cancer disease is an imperative, challenging problem in medical applications. In this paper, a proposed model to improve the accuracy of classification algorithms is presented. A new approach for designing effective pre-processing stage is introduced. Such approach integrates K-means clustering algorithm with fuzzy rough feature selection or correlation feature selection for data reduction. The attributes of the reduced clustered data are merged to form a new data set to be classified. Simulation results prove the enhancement of classification by using the proposed approach. Moreover, a new hybrid model for classification composed of K-means clustering algorithm, fuzzy rough feature selection and discernibility nearest neighbour is achieved. Compared to previous studies on the same data, it is proved that the presented model outperforms other classification models. The proposed model is tested on breast cancer dataset from UCI machine learning repository.

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Comparative Study among Data Reduction Techniques over Classification Accuracy

El‐Hasnony¹,

Bakry²,

Saleh³

2015

IJCA

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Nowadays, Healthcare is one of the most critical issues that need efficient and effective analysis. Data mining provides many techniques and tools that help in getting a good analysis for healthcare data. Data classification is a form of data analysis for deducting models. Mining on a reduced version of data or a lower number of attributes increases the efficiency of system providing almost the same results. In this paper, a comparative study between different data reduction techniques is introduced. Such comparison is tested against classification algorithms accuracy. The results showed that fuzzy rough feature selection outperforms rough set attribute selection, gain ratio, correlation feature selection and principal components analysis.

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A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

Cited by 3 publications

References 32 publications

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

A Proposed Hybrid Effective Technique for Enhancing Classification Accuracy

Comparative Study among Data Reduction Techniques over Classification Accuracy

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