Novel Incremental Algorithms for Attribute Reduction From Dynamic Decision Tables Using Hybrid Filter–Wrapper With Fuzzy Partition Distance

Giang, Nguyễn Long; Sơn, Lê Hoàng; Ngân, Trần Thị; Tuấn, Trần Mạnh; Phuong, Ho Thi; Abdel‐Basset, Mohamed; Macedo, Antonio Roberto L. de; Albuquerque, Victor Hugo C. de

doi:10.1109/tfuzz.2019.2948586

Cited by 37 publications

(9 citation statements)

References 52 publications

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“…To verify the performance of the proposed incrementally mining CCB method, it is tested on 5 datasets. The experiment scheme is similar to the scheme in [32]. Firstly, the whole dataset is equally divided into disjoint two subsets, namely, the original dataset and incremental dataset.…”

Section: Experimental Settingsmentioning

confidence: 99%

Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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Bicluster mining has been frequently studied in the data mining field. Because column constant biclusters (CCB) can be transformed to be discriminative rules, they have been widely applied in various fields. However, no research on incrementally mining CCB has been reported in the literature. In real situations, due to the limitation of computation resources (such as memory), it is impossible to mine biclusters from very large datasets. Therefore, in this study, we propose an incremental mining CCB method. CCB can be deemed as a special case of frequent pattern (FP). Currently the most frequently used method for incrementally mining frequent patterns is FP tree based method. In this study, we innovatively propose an incremental mining CCB method with modified FP tree data structure. The technical contributions lie in two aspects. The first aspect is that we propose a modified FP tree data structure, namely Feature Value Sorting Frequent Pattern (FVSFP) tree that can be easily maintained. The second aspect is that we innovatively design a method for mining CCB from FVSFP tree. To verify the performance of the proposed method, it is tested on several datasets. Experimental results demonstrated that the proposed method has good performance for incrementally handling a newly added dataset.

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Section: Experimental Settingsmentioning

confidence: 99%

Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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“…These concepts will be an essential foundation for proposing an attribute reduction algorithm presented in the third part of the paper. Some basic concepts can be cited in [3], [31], [35]- [38].…”

Section: Preliminariesmentioning

confidence: 99%

“…Then they proposed a filtering algorithm (IFPR) to select attributes on the decision table. Experimental results show that the IFPR algorithm performs superior to the algorithms [31]- [34] according to the FRS approach. Giang et al in work [35] recently constructed a distance measure based on the intuitionistic fuzzy set (IFS) model and proposed the IFDBAR algorithm to find the reduct on the decision table.…”

Section: Introductionmentioning

confidence: 99%

On Distance-Based Attribute Reduction With α, β-Level Intuitionistic Fuzzy Sets

Anh,

Thuy,

Thi

et al. 2023

IEEE Access

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“…There are two general feature selection strategies: wrappers [36] and filters [35]. While the wrapper strategy employs learning algorithms to evaluate selected attribute subsets, the Filter strategy selects attributes based on some measures such as information gain [24,26,27,28,29,33,34,39,40], consistency [1,23,25,30,47], distance [8,9,35,42,43], and dependency [27, 41,38,46]. These measures can be classified into distance and positive regions [30].…”

Section: Introductionmentioning

confidence: 99%

“…Making immediate attribute reduction in the numerical decision table is popular in attribute reduction, with the fuzzy set (FS) and IFS being popular sets. In the FS, many algorithms use different fuzzy measures such as fuzzy positive region [44,45,46,47], fuzzy entropy [26], and fuzzy distance [8]. Results show that the size and accuracy of classification of the reduct are gained, but for the noisy datasets, the IFS were often opted due to the nature of some constraints in the IFS approximation space [3, ?].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Attribute Reduction from Decision Table based on Intuitionistic Fuzzy Topology

Dai¹,

Giang

Thi

et al. 2022

Preprint

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Most of the current attribute reduction methods use the measure to define the reduct, such as positive region of rough set theory (RST), granular information entropy, and granular distance measures. However, the reducts defined based on the measures are still limited in sizes because of the information conservation property of the reduct compared to the original dataset. Dealing with this matter, this paper proposes a new approach using the intuitionistic fuzzy topology (IFT) to develop an attribute reduction method to find the reduct with the optimal size through topology. Specifically, the IFT base and IFT sub-base structures are proposed based on the pre-order relation. Secondly, we propose a new measure to evaluate the significance of the attribute based on the IFT sub-base. Lastly, we propose a new definition of the reduct based on the IFT base and design two new algorithms based on Heuristic approaches. The theoretical and experimental results show that the proposed method is efficient in size and accuracy of the reduct

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Novel Incremental Algorithms for Attribute Reduction From Dynamic Decision Tables Using Hybrid Filter–Wrapper With Fuzzy Partition Distance

Cited by 37 publications

References 52 publications

Incrementally Mining Column Constant Biclusters with FVSFP Tree

Incrementally Mining Column Constant Biclusters with FVSFP Tree

On Distance-Based Attribute Reduction With α, β-Level Intuitionistic Fuzzy Sets

A New Approach for Attribute Reduction from Decision Table based on Intuitionistic Fuzzy Topology

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