Open Continual Feature Selection via Granular-Ball Knowledge Transfer

This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown classes may emerge. CFS encounters two primary challenges: the discovery of unknown knowledge and the transfer of known knowledge. To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection. CFS consists of two stages: initial learning and open learning. The former aims to establish an initial knowledge base through multi-granularity representation using granular-balls. The latter utilizes prior granular-ball knowledge to identify unknowns, updates the knowledge base for granular-ball knowledge transfer, reinforces old knowledge, and integrates new knowledge. Subsequently, we devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period. Extensive experimental results on public benchmark datasets demonstrate our method's superiority in terms of both effectiveness and efficiency compared to state-of-the-art feature selection methods.

Multi-granularity Causal Structure Learning

Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Multi-granularity Causal Structure Learning), which first leverages sparse auto-encoder to explore coarse-graining strategies and causal abstractions from micro-variables to macro-ones. MgCSL then takes multi-granularity variables as inputs to train multilayer perceptrons and to delve the causality between variables. To enhance the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity constraint to adeptly search the directed acyclic graph among variables. Experimental results show that MgCSL outperforms competitive baselines, and finds out explainable causal connections on fMRI datasets.

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB, instead of randomly selecting the center and calculating the distances between it to all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, a $k$-nearest neighbors algorithm (GB$k$NN++) which can reduce misclassification at class boundary to some extent is presented. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $20$ public benchmark data sets.

Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

Human cognition has a ``large-scale first'' cognitive mechanism, therefore possesses adaptive multi-granularity description capabilities. This results in computational characteristics such as efficiency, robustness, and interpretability. Although most existing artificial intelligence learning methods have certain multi-granularity features, they do not fully align with the ``large-scale first'' cognitive mechanism. Multi-granularity granular-ball computing is an important model method developed in recent years. This method can use granular-balls of different sizes to adaptively represent and cover the sample space, and perform learning based on granular-balls. Since the number of coarse-grained "granular-ball" is smaller than the number of sample points, granular-ball computing is more efficient; the coarse-grained characteristics of granular-balls are less likely to be affected by fine-grained sample points, making them more robust; the multi-granularity structure of granular-balls can produce topological structures and coarse-grained descriptions, providing natural interpretability. Granular-ball computing has now been effectively extended to various fields of artificial intelligence, developing theoretical methods such as granular-ball classifiers, granular-ball clustering methods, granular-ball neural networks, granular-ball rough sets, and granular-ball evolutionary computation, significantly improving the efficiency, noise robustness, and interpretability of existing methods. It has good innovation, practicality, and development potential. This article provides a systematic introduction to these methods and analyzes the main problems currently faced by granular-ball computing, discussing both the primary applicable scenarios for granular-ball computing and offering references and suggestions for future researchers to improve this theory.

Granular ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

Human cognition has a ``large-scale first'' cognitive mechanism, therefore possesses adaptive multi-granularity description capabilities. This results in computational characteristics such as efficiency, robustness, and interpretability. Although most existing artificial intelligence learning methods have certain multi-granularity features, they do not fully align with the ``large-scale first'' cognitive mechanism. Multi-granularity granular-ball computing is an important model method developed in recent years. This method can use granular-balls of different sizes to adaptively represent and cover the sample space, and perform learning based on granular-balls. Since the number of coarse-grained "granular-ball" is smaller than the number of sample points, granular-ball computing is more efficient; the coarse-grained characteristics of granular-balls are less likely to be affected by fine-grained sample points, making them more robust; the multi-granularity structure of granular-balls can produce topological structures and coarse-grained descriptions, providing natural interpretability. Granular-ball computing has now been effectively extended to various fields of artificial intelligence, developing theoretical methods such as granular-ball classifiers, granular-ball clustering methods, granular-ball neural networks, granular-ball rough sets, and granular-ball evolutionary computation, significantly improving the efficiency, noise robustness, and interpretability of existing methods. It has good innovation, practicality, and development potential. This article provides a systematic introduction to these methods and analyzes the main problems currently faced by granular-ball computing, discussing both the primary applicable scenarios for granular-ball computing and offering references and suggestions for future researchers to improve this theory.

Granular-ball Optimization Algorithm

The existing intelligent optimization algorithms are designed based on the finest granularity, i.e., a point. This leads to weak global search ability and inefficiency. To address this problem, we proposed a novel multi-granularity optimization algorithm, namely granular-ball optimization algorithm (GBO), by introducing granular-ball computing. GBO uses many granular-balls to cover the solution space. Quite a lot of small and fine-grained granular-balls are used to depict the important parts, and a little number of large and coarse-grained granular-balls are used to depict the inessential parts. Fine multi-granularity data description ability results in a higher global search capability and faster convergence speed. In comparison with the most popular and state-of-the-art algorithms, the experiments on twenty benchmark functions demonstrate its better performance. The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.

Sketch Less Face Image Retrieval: A New Challenge

In some specific scenarios, face sketch was used to identify a person. However, drawing a complete face sketch often needs skills and takes time, which hinder its widespread applicability in the practice. In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig.1). Firstly, we developed a method to generate the data of sketch with drawing process, and opened such dataset; Secondly, we proposed a two-stage method as the baseline for SLFIR that (1) A triplet network, was first adopt to learn the joint embedding space shared between the complete sketch and its target face photo; (2) Regarding the sketch drawing episode as a sequence, we designed a LSTM module to optimize the representation of the incomplete face sketch. Experiments indicate that the new framework can finish the retrieval using a partial or pool drawing sketch.

A novel cluster internal evaluation index based on hyper-balls

It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.