Mobile message-aware enhancement using fuzzy lattice reasoning

Al-Sultany, Ghaidaa A.; Li, Maozhen; Ponraj, Mahesh; Al-Raweshidy, H.S.

doi:10.1109/fskd.2012.6234315

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Our experiments on real data sets are closely modeled on that of Fang et al [17]. The performance of the classifier of single Choquet integral and the classifier of cross-oriented Choquet integrals have been compared with those of ten classification methods, which are NaïveBayes [20], BayesNet [21], NBtree [22,23], Classification via regression [24,25], Sequential Minimal Optimization (SMO) [26,27], Radial Basis Function (RBF) network [28,29], Decision tree, Fuzzy lattice Reasoning classifier [30][31][32], Fuzzy decision tree [33,34], and Choquet distance based classifier with penalty. The details of above methods and the result analysis are reported in [17].…”

Section: Tests On Real Datamentioning

confidence: 99%

Cross-oriented choquet integrals and their applications on data classification

Yang¹,

Wang

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Compared to the classification model based on single Choquet integrals, a novel generalized nonlinear classification model based on cross-oriented Choquet integrals is presented. A couple of Choquet integrals are used to achieve the classification boundaries which can classify data in situations such as one class surrounding another one in a high dimensional space. The classification problems come down to properly specifying the fuzzy measure with respect to which the Choquet integral(s) are defined and the classifying boundaries by which the different classes are separated. The values of these unknown parameters are optimally determined by an evolutionary computation. The performance of the presented model is compared and validated with some existed methods on a number of benchmark datasets.

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