A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a maximal dependency function. It does not fit a given dataset well and cannot ideally describe the differences in sample classification. Therefore, in this study, we introduce a new model for handling this problem. First, we define the fuzzy decision of a sample using the concept of fuzzy neighborhood. Then, a parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced. This can guarantee that the membership degree of a sample to its own category reaches the maximal value. Furthermore, this approach can fit a given dataset and effectively prevents samples from being misclassified. Finally, we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection. Twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms, and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets, especially for those datasets for which different categories exhibit a large degree of overlap.

A Fitting Model for Feature Selection With Fuzzy Rough Sets

Rough set theory has been extensively discussed in machine learning and pattern recognition. It provides us another important theoretical tool for feature selection. In this paper, we construct a novel rough set model for feature subset selection. First, we define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. A parameterized fuzzy relation is introduced to characterize fuzzy information granules for analysis of real-valued data. Then, we use the relationship between fuzzy neighborhood and fuzzy decision to construct a new rough set model: fuzzy neighborhood rough set model. Based on this model, the definitions of upper and lower approximation, boundary region and positive region are given, and the effects of parameters on these concepts are discussed. To make the new model tolerate noises in data, we introduce a variable-precision fuzzy neighborhood rough set model. This model can decrease the possibility that a sample is classified into a wrong category. Finally, we define the dependency between fuzzy decision and condition attributes and employ the dependency to evaluate the significance of a candidate feature, using which a greedy feature subset selection algorithm is designed. The proposed algorithm is compared with some classical algorithms. The experiments show that the proposed algorithm gets higher classification performance and the numbers of selected features are relatively small.

Feature subset selection based on fuzzy neighborhood rough sets

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms.

Feature Selection Based on Neighborhood Discrimination Index

Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature selection as the rough set theory is, it can only handle symbolic attributes. In order to overcome this drawback, the fuzzy rough set model is proposed, which is an extended model of rough sets and is able to deal with imprecision and uncertainty in both symbolic and numerical attributes. The existing attribute selection algorithms based on the fuzzy rough set model mainly take the angle of “attribute set,” which means they define the object function representing the predictive ability for an attribute subset with regard to the domain of discourse, rather than following the view of an “object pair.” Algorithms from the viewpoint of the object pair can ignore the object pairs that are already discerned by the selected attribute subsets and, thus, need only to deal with part of object pairs instead of the whole object pairs from the discourse, which makes such algorithms more efficient in attribute selection. In this paper, we propose the concept of reduced maximal discernibility pairs, which directly adopts the perspective of the object pair in the framework of the fuzzy rough set model. Then, we develop two attribute selection algorithms, named as reduced maximal discernibility pairs selection and weighted reduced maximal discernibility pair selection, based on the reduced maximal discernibility pairs. Experiment results show that the proposed algorithms are effective and efficient in attribute selection.

Maximal-Discernibility-Pair-Based Approach to Attribute Reduction in Fuzzy Rough Sets

Attribute reduction is one of the most important applications of fuzzy rough sets in machine learning and pattern recognition. Most existing methods employ the intersection operation of fuzzy relations to construct the dependency function of attribute reduction. However, the intersection operation may lead to low discrimination of fuzzy decision in high-dimensional data space. In this study, we introduce distance measures into fuzzy rough sets and propose a novel method for attribute reduction. We first construct a fuzzy rough set model based on distance measure with a fixed parameter. Then, the fixed distance parameter is replaced by a variable one to better characterize attribute reduction with fuzzy rough sets. Some iterative formulas for computing fuzzy rough dependency and attribute significance are presented, and an iterative computation model based on a variable distance parameter is proposed. Based on this, a greedy convergent algorithm for attribute reduction is designed. The experimental comparison demonstrates that the proposed reduction algorithm is effective and performs better than some of the other existing algorithms.

Fuzzy rough set-based attribute reduction using distance measures

Attribute reduction is one of the most important methods for feature selection in machine learning researches. In this work, a new fuzzy rough set model based on distance measures is proposed and the fuzzy dependency function is constructed. Then, the significance measure of a candidate attribute is defined, by which a greedy forward algorithm for attribute reduction is designed. The proposed algorithm is compared with several existing algorithms using UCI data sets. Experimental results show that the proposed reduction algorithm is feasible and effective.

Attribute reduction using distance-based fuzzy rough sets

Diffusion models (DMs) is a new generative model based on deep learning with computational vision, which has been applied to image generation, image enhancement, image restoration, text-to-image and other fields. Compared with traditional autoregressive models, variational autoencoders (VAEs), energy-based models (EBMs), flow-based models (FBMs) and generative adversarial network (GAN) models, diffusion models can generate images with strong diversity and significant details, and have many advantages such as easier training and flexibility compared with generative adversarial networks. This paper mainly summarizes the existing image generation models based on diffusion model, compares them, and proposes possible improvement schemes.

Comparison of Image Generation methods based on Diffusion Models

In recent years, Unmanned Aerial Vehicle (UAV) has played an important role in the field of wireless communication with its high mobility and high controllability. In this paper, we focus on the application of deep reinforcement learning (DRL) in UAV resource allocation and trajectory planning. DRL is used to design the decision deployment of UAVs, optimize data transfer rate, throughput, energy efficiency and other metrics, and learn better path planning strategies to make UAVs’ decisions more intelligent. We summarize the reinforcement learning (RL) methods, describe the relevant research in the field of UAV wireless communication in recent years, and investigate the algorithmic mechanisms of DRL such as deep Q network (DQN) and deep deterministic policy gradient (DDPG) algorithm.

Yali Qi

Papers

A Fitting Model for Feature Selection With Fuzzy Rough Sets

Feature subset selection based on fuzzy neighborhood rough sets

Attribute reduction using distance-based fuzzy rough sets

Comparison of Image Generation methods based on Diffusion Models

A Review of Research on the Application of Deep Reinforcement Learning in Unmanned Aerial Vehicle Resource Allocation and Trajectory Planning