Sequential three-way decision and granulation for cost-sensitive face recognition

Li, Huaxiong; Zhang, Libo; Huang, Bing; Zhou, Xianzhong

doi:10.1016/j.knosys.2015.07.040

Cited by 242 publications

(40 citation statements)

References 58 publications

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“…We observe that (18), (22) and (27) are satisfied and, in consequence, α = 0.7 and β = 0.65 by equations (19) and (21). Finally, we obtain the decision regions of the fuzzy event A …”

Section: Accepted Manuscriptmentioning

confidence: 86%

“…The DTRS approach approximates a given concept or a set by three regions (positive, negative and boundary regions) which correspond to positive, negative and boundary rules in threeway decisions (3WDs), respectively [11,50,51]. It has been applied to text classification [20,23], web-based support systems [54], cluster analysis [27,58], investment decisions [30], multi-classification [6,26,27], email filtering [17,63], government decisions [28], face recognition [22] etc. In the following, we first review developments of PRSs, DTRSs and 3WDs; then we present the main work of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy probabilistic rough sets and their corresponding three-way decisions

Zhao

2016

Knowledge-Based Systems

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“…We observe that (18), (22) and (27) are satisfied and, in consequence, α = 0.7 and β = 0.65 by equations (19) and (21). Finally, we obtain the decision regions of the fuzzy event A …”

Section: Accepted Manuscriptmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Fuzzy probabilistic rough sets and their corresponding three-way decisions

Zhao

2016

Knowledge-Based Systems

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“…To handle the nonmonotonicity of probabilistic positive region, Li and Zhou proposed a nonmonotonic attribute reduction for the DTRS model. It is not necessary for non-monotonic attribute reducts to induce the same positive region as the original attributes [30,31]. To improve the efficiency of probabilistic attribute reduction, Meng and Shi proposed a fast algorithm to compute the attribute reducts in DTRS model, in particular, the improvements include a fast division approach for equivalence class computation, a heuristic strategy of attribute selection and the post-processing techniques of super-reducts [39].…”

Section: Three-way Decision Theorymentioning

confidence: 99%

Three-way decision support for diagnosis on focal liver lesions

Chen

Yue

Fujita

et al. 2017

Knowledge-Based Systems

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“…Firstly, we demonstrated that the probabilistic model of the piecewise-regular object is suitable to define the granularity levels. In comparison with other granularity definitions used in image recognition [8], our approach makes it possible to classify the new observation very fast, because the number of segments in the coarse-grained granules is usually low. Moreover, we paid attention to the practically important case of large number of classes.…”

Section: Fast Classification Of the Phogsmentioning

confidence: 99%

Hierarchical Intelligent Classification Systems

Savchenko

2016

SpringerBriefs in Optimization

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This chapter is focused on the insufficient performance of the hierarchical classifiers. The theory of granular computing and the mathematical model of the piecewise-regular object from Chap. 2 are used to define the hierarchical levels. The sequence with a small number of the weakly homogeneous segments is considered as a coarse-grained granule. Fine-grained granules stand for a large number of highhomogeneous small segments. We apply sequential three-way decisions (TWD) to speed-up the classification procedure. To improve the classification performance at each granularity level, the probabilistic rough set of the distance between objects from different classes at each level is created. If the distance between an observed object and the next checked instance is included in its negative region, the search procedure is terminated. Experimental results demonstrated that sequential TWD significantly decreases the classification time in comparison with the matching of the pyramid histograms of oriented gradients. Granular Computing and Hierarchical Intelligent Classification SystemIt is known that the theory of granular computing [10,20] can be used to significantly improve the recognition quality of the audiovisual data by its examination at various granulation levels [5,7] in a hierarchical classification system [6]. It is the key idea of the CNN, which has been recently proved to reach the quality of human recognition in several image recognition tasks [3]. One of the most practically important examples of the potential of granular computing in the classification task is the PHOG (Pyramid HOG) image descriptor [1]. According to this method, an image is divided at each granularity level into a regular grid of blocks and the HOG is computed in each block. The pyramid of grids is built, and the HOGs of the input and reference objects are compared at each level of hierarchy (pyramid). The final distance between objects is calculated as a weighted sum of distances between the HOGs in the pyramid. It was shown [1] that this method allowed to achieve 10 %-higher accuracy in comparison with the state-of-the-art HOG [4]. Let us discuss the hierarchical classification of the piecewise-regular objects (Chap. 2) in detail.

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Sequential three-way decision and granulation for cost-sensitive face recognition

Cited by 242 publications

References 58 publications

Fuzzy probabilistic rough sets and their corresponding three-way decisions

Fuzzy probabilistic rough sets and their corresponding three-way decisions

Three-way decision support for diagnosis on focal liver lesions

Hierarchical Intelligent Classification Systems

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