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Feature selection is an effective method to simplify data analysis and obtain key features, which improves the accuracy and generalization ability of classifiers. Neighbourhood rough set is a typical granular computing model that enables data ...

Uncertainty quantification of belief functions is an unsolved issue in belief function theory that is used widely to tackle epistemic uncertainty. This study provides a new definition of conditional entropy of belief functions, called conditional ...

Dempster-Shafer (D-S) evidence theory has extensive applications in the field of data fusion. It uses the mass function to replace the probability distribution in Bayesian Probability theory, which has the advantages of weak constraints and ...

Attribute reductions eliminate redundant information to become valuable in data reasoning. In the data context of interval-set decision systems (ISDSs), attribute reductions rely on granulation structures and uncertainty measures; however, the ...

Universal Domain Adaptation (UniDA) is a technology that enables the intelligent model to transfer knowledge learned from labeled source domains to related but unlabeled target domains without any prior label set relationship. The key to UniDA ...

Anomaly detection has been used in a wide range of fields. However, most of the current detection methods are only applicable to certain data, ignoring uncertain information such as fuzziness in the data. Fuzzy rough set theory, as an essential ...

As an important extension of the rough set theory, interval-set rough sets provide an effective method to solve the problem that the objective sets cannot be accurately expressed in the rough approximation process and to induce classification ...

Non-standard fuzzy sets play a significant role in uncertainty modeling. In addition to membership and non-membership degree, how to handle the hesitant degree is the key issue in the uncertain information process. In this paper, we model the ...

• A novel entropy measure for PFSs was defined and proposed.
• Comparisons made with other state-of-art approaches.
• The TOPSIS method with the presented entropy measure was applied to fault diagnosis.
• Medical diagnoses were more ...

Uncertainty measures exhibit algebraic and informational perspectives, and the two-view measure integration facilitates feature selections in classification learning. According to neighborhood decision systems (NDSs), two basic algorithms of ...

• Three-way algebraic and informational measures induce three-way fusion measures.
• Three-way fusion measures acquire granulation monotonicity and nonmonotonicity.
• Three-level feature selections offer 4 × 3 = 12 monotonic/nonmonotonic ...

Information entropy theory has been widely studied and successfully applied to machine learning and data mining. The fuzzy entropy and neighborhood entropy theories have been rapidly developed and widely used in uncertainty measure. In ...

Generalized quantum evidence theory (GQET) is effective for uncertainty reasoning from the view of the quantum framework in an open world. However, how to construct the quantum model of GQET to bridge evidence theory and quantum theory ...

• A new quantum model for generalized quantum mass function in GQET is proposed.
• ...

Dempster Shafer (DS) theory, an extension of probability theory, is widely used for modeling uncertainty in information. It is based on the concept of basic probability assignment. Each basic probability value has a corresponding ...

We consider a wide range of measures of uncertainty that have been proposed within the Dempster–Shafer evidence theory. All these measures aim to quantify the uncertainty associated with a given basic probability assignment. As a ...

The classical ID3 decision tree algorithm cannot directly handle continuous data and has a poor classification effect. Moreover, most of the existing approaches use a single mechanism for node measurement, which is unfavorable for the construction ...

Abstract. Since Shannon put forward information entropy and used it to measure the amount of information in the information system, people began to explore various new methods to measure the uncertainty in the information system. Rough set is a method ...

Fuzzy rough set theory has attracted much attention because of its successful application in uncertainty measurement. To improve the efficiency and robustness of uncertainty measure using the theory, robust fuzzy rough approximations ...

The probabilistic hesitant fuzzy sets (PHFSs), which have received a lot of attention and have been widely used in many domains, are effective in describing hesitant evaluations. Despite significant progress, entropy and cross-entropy, ...

Neighborhood rough set theory is widely used to measure the uncertainty of data in machine learning and data mining. However, the neighborhood radius has a significant influence on the effectiveness and robustness of the models and ...

Attribute reduction is nowadays an extremely important data preprocessing technique in the field of data mining, which has gained much attention due to its ability to provide better generalization performance and learning speed for analysis ...

• Horizontal partitioning method for neighborhood-entropy computation is proposed.
• Conceptual parallelization logic of attribute reduction is presented.
• MapReduce accelerated attribute reduction algorithm is implemented using Spark.

In fuzzy decision-making, the probabilistic linguistic term sets (PLTSs) are flexible in depicting people’s linguistic evaluations. As the uncertainty measures of PLTSs, entropy and cross-entropy are the important decision-making tools. Generally ...