Fuzzy Granular-Ball Computing Framework and Its Implementation in SVM

• URL: http://arxiv.org/abs/2210.11675v1
• Date: Fri, 21 Oct 2022 02:03:52 GMT
• Title: Fuzzy Granular-Ball Computing Framework and Its Implementation in SVM
• Authors: Shuyin Xia, Xiaoyu Lian, Yabin Shao
• Abstract summary: We propose a framework for a fuzzy granular-ball computational classifier by introducing granular-ball computing into fuzzy set. The computational framework is based on the granular-balls input rather than points. The framework is extended to the fuzzy support vector machine (FSVM), and granular ball fuzzy SVM (GBFSVM) is derived.
• Score: 0.8916420423563476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing fuzzy computing methods use points as input, which is the finest granularity from the perspective of granular computing. Consequently, these classifiers are neither efficient nor robust to label noise. Therefore, we propose a framework for a fuzzy granular-ball computational classifier by introducing granular-ball computing into fuzzy set. The computational framework is based on the granular-balls input rather than points; therefore, it is more efficient and robust than traditional fuzzy methods. Furthermore, the framework is extended to the fuzzy support vector machine (FSVM), and granular ball fuzzy SVM (GBFSVM) is derived. The experimental results demonstrate the effectiveness and efficiency of GBFSVM.

Related papers

• Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing [0.2999888908665658]
  In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art models. LSTSVM suffers from sensitivity to noise and inversions, overlooking the principle and instability in resampling. We propose the robust granular ball LSTSVM (GBLSTSVM), which is trained using granular balls instead of original data points.
  arXiv  Detail & Related papers  (2024-10-22T18:13:01Z)
• Granular-Balls based Fuzzy Twin Support Vector Machine for Classification [12.738411525651667]
  We introduce the granular-ball twin support vector machine (GBTWSVM) classifier, which integrates granular-ball computing (GBC) with the twin support vector machine (TWSVM) We design the membership and non-membership functions of granular-balls using Pythagorean fuzzy sets to differentiate the contributions of granular-balls in various regions.
  arXiv  Detail & Related papers  (2024-08-01T16:43:21Z)
• Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels [57.46832672991433]
  We propose a novel equation discovery method based on Kernel learning and BAyesian Spike-and-Slab priors (KBASS) We use kernel regression to estimate the target function, which is flexible, expressive, and more robust to data sparsity and noises. We develop an expectation-propagation expectation-maximization algorithm for efficient posterior inference and function estimation.
  arXiv  Detail & Related papers  (2023-10-09T03:55:09Z)
• Projection based fuzzy least squares twin support vector machine for class imbalance problems [0.9668407688201361]
  We propose a novel fuzzy based approach to deal with class imbalanced as well noisy datasets. The proposed algorithms are evaluated on several benchmark and synthetic datasets.
  arXiv  Detail & Related papers  (2023-09-27T14:28:48Z)
• Multiplierless In-filter Computing for tinyML Platforms [6.878219199575747]
  We present a novel multiplierless framework for in-filter acoustic classification. We use MP-based approximation for training, including backpropagation mitigating approximation errors. The framework is more efficient than traditional classification frameworks with just less than 1K slices.
  arXiv  Detail & Related papers  (2023-04-24T04:33:44Z)
• Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method [54.2899493638937]
  Human cognition operates on a "Global-first" cognitive mechanism, prioritizing information processing based on coarse-grained details. The analysis pattern reliance on the finest granularity and single-granularity makes most existing computational methods less efficient, robust, and interpretable. Multi-granularity granular-ball computing employs granular-balls of varying sizes to daptively represent and envelop the sample space. Granular-ball computing is a rare and innovative theoretical approach in AI that can adaptively and simultaneously enhance efficiency, robustness, and interpretability.
  arXiv  Detail & Related papers  (2023-04-21T03:26:29Z)
• Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls [67.33923111887933]
  In this paper, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios.
  arXiv  Detail & Related papers  (2023-03-07T01:52:55Z)
• GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing [78.92205914422925]
  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" Experimental results on several data sets demonstrate the power of the algorithm.
  arXiv  Detail & Related papers  (2023-03-02T09:04:35Z)
• An Efficient and Adaptive Granular-ball Generation Method in Classification Problem [69.02474089703678]
  Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method.
  arXiv  Detail & Related papers  (2022-01-12T07:26:19Z)
• Estimating Average Treatment Effects with Support Vector Machines [77.34726150561087]
  Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We adapt SVM as a kernel-based weighting procedure that minimizes the maximum mean discrepancy between the treatment and control groups. We characterize the bias of causal effect estimation arising from this trade-off, connecting the proposed SVM procedure to the existing kernel balancing methods.
  arXiv  Detail & Related papers  (2021-02-23T20:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.