An Ensemble Framework to Forest Optimization Based Reduct Searching

Jin, Wang; Liu, Yuxin; Chen, Jianjun; Yang, Xibei

doi:10.3390/sym14061277

“…The key distinction lies in that neighborhood rough sets are established on the basis of neighborhood relations, as opposed to relations of indiscernibility [19]. Hence, the neighborhood rough set model is capable of processing both discrete and contin-uous data [20]. Moreover, the partitioning of neighborhoods granulates the sample space, which can reflect the discriminative power of different attributes on the samples [21].…”

Section: Neighborhood Rough Setsmentioning

confidence: 99%

“…In the experiment, the proposed measure is compared with six advanced attribute reduction algorithms as well as with the algorithm without applying any attribute reduction methods (no reduct) using Regression Trees (CART) [20], K-Nearest Neighbors (KNN, K = 3) [39], and Support Vector Machines (SVM) [40]. The performance of the reducer is evaluated in aspects of the stability, accuracy, and timeliness of classification, as well as the stability of reduction.…”

Section: Experimental Configurationmentioning

confidence: 99%

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Fan,

Chen,

Cui

et al. 2024

Mathematics

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Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field.

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“…Hence, the neighborhood rough set model is capable of processing both discrete and continuous data. [36] Moreover, the partitioning of neighborhoods granulates the sample space, which can reflect the discriminative power of different attributes on the samples.…”

Section: Neighborhood Rough Setsmentioning

confidence: 99%

“…In the experiment, the proposed measure is compared with six advanced attribute reduction algorithms using Regression Trees (CART) [36], K-Nearest Neighbors (KNN, K=3) [41], and Support Vector Machines (SVM) [37]. The performance of the reducer is evaluated in aspects of the stability, accuracy, and timeliness of classification, as well as the stability of reduction.…”

Section: Experimental Configurationmentioning

confidence: 99%

Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model

Fan,

Chen,

Cui

et al. 2024

Preprint

0

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Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

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“…For instance, considering the technique of attribute reduction in multi-label problem, Liu et al [15] designed a neighborhood granulation attribute reduction algorithm that fuses various concepts; Chen et al [16] used label specific features algorithm and sample section strategy with randomness to devise a new type of algorithm of feature reduction. Considering the ensemble learning problem, Wang et al [17] introduced the forest optimization algorithm into the process of picking up reduct which can return multiple reducts, and used these reducts to develop an ensemble framework for executing voting classification over testing samples. Considering the monotonic classification problem, Zhang et al [18] applied the matrix approach for lower approximation in an inconsistent decision system to give the discriminative con-cept tree with the relations by dominance, and then fused the evaluation functions by tree approach to establish an efficient algorithm of searching lower approximation reduct.…”

mentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

0

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As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature's significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

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An Ensemble Framework to Forest Optimization Based Reduct Searching

Cited by 6 publications

References 52 publications

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

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