Article
Computer Science, Information Systems
Zhihong Wang, Hongmei Chen, Xiaoling Yang, Jihong Wan, Tianrui Li, Chuan Luo
Summary: Dimensionality reduction is an important step in many learning methods to achieve optimal performance using discriminative features. This study proposes a fuzzy rough dimensionality reduction method that combines feature selection and feature extraction, and compares its performance with other algorithms, showing higher classification performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiaoling Yang, Hongmei Chen, Tianrui Li, Pengfei Zhang, Chuan Luo
Summary: This paper investigates the feature selection problem in fuzzy rough set theory and proposes a new feature selection model based on Student-t kernel and fuzzy divergence. Experimental results demonstrate the effectiveness of the proposed model on real-world datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Xiaoling Yang, Binbin Sang
Summary: Feature selection is a crucial data preprocessing approach in data mining, and the interaction between features and their dynamic changes should be taken into consideration to prevent the loss of useful information.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
Shivam Shreevastava, Priti Maratha, Tanmoy Som, Anoop Kumar Tiwari
Summary: This article proposes an innovative framework called intuitionistic fuzzy rough set (IFRS) to deal with uncertainty in judgement and identification. It integrates IF set and rough set with the concept of (alpha, beta)-indiscernibility to avoid noise. The framework is supported with proofs and a feature selection method is presented using this framework. A concrete illustration and comparative study with other methods are provided to demonstrate the effectiveness and superiority of the proposed technique.
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Tiwari, Tanmoy Som
Summary: This paper introduces a technique for missing value imputation and feature selection using fuzzy rough set-based approaches. The experimental results demonstrate its high applicability and robustness, as well as its ability to significantly reduce data dimensionality while maintaining high performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper discusses the importance of feature selection in the telecommunications industry for machine learning models. It introduces a new approach that combines ant colony optimization and reptile search algorithm, and evaluates its performance in customer churn prediction.
Article
Computer Science, Artificial Intelligence
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu
Summary: This study proposes a label enhancement-based feature selection method using ant colony optimization for multilabel data. The method effectively reflects the correlations between features and labels, and achieves significant classification effects in experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Pei Liang, Dingfei Lei, KwaiSang Chin, Junhua Hu
Summary: The current research on fuzzy rough sets for feature selection faces two major problems: the difficulty in evaluating the importance of feature subsets accurately in high-dimensional data space due to the use of multiple intersection operations of fuzzy relations, and the sensitivity to noisy information in the classical fuzzy rough sets model. To address these issues, this study proposes a radial basis function kernel-based similarity measure and introduces a relative classification uncertainty measure to improve the robustness of the fuzzy rough sets model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ramesh Kumar Huda, Haider Banka
Summary: Feature selection is the process of selecting criterion functions and search strategies to find the best feature subset from a large number of subsets. Algorithms based on particle swarm optimization and fuzzy rough fitness function can effectively select optimal feature subsets from datasets with numerous features.
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Binbin Sang, Zhong Yuan
Summary: This study proposes a method for feature selection in data with uncertainty, fuzziness, and noise. A robust fuzzy rough set model is constructed to enhance the robustness and antinoise ability. Uncertainty measures are defined to analyze the interactivity and redundancy of features. Experimental results demonstrate the significance of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Anhui Tan, Jiye Liang, Wei-Zhi Wu, Jia Zhang, Lin Sun, Chao Chen
Summary: Fuzzy rough set and discernibility matrix have been successfully applied in single-label learning, but their application in multi-label data requires further investigation. This paper introduces the fuzzy rough discrimination matrix to solve the problem of multi-label feature selection, effectively evaluating feature discrimination ability by capturing label correlations and achieving promising experimental results.
Article
Computer Science, Artificial Intelligence
Xiaoling Yang, Hongmei Chen, Tianrui Li, Chuan Luo
Summary: The study focuses on the robust fuzzy rough set approach for feature selection, proposing a Noise-aware Fuzzy Rough Sets (NFRS) model and constructing an evaluation function considering relevance and redundancy. Experimental results demonstrate the effectiveness and superiority of the proposed model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Binbin Sang, Lei Yang, Hongmei Chen, Weihua Xu, Xiaoyan Zhang
Summary: This paper proposes a robust fuzzy dominance rough set model to tackle noise interference and develops a feature selection method based on this model for ordinal classification tasks. The robust model is constructed using a non-linear vague quantifier and related dependency function, while the contribution of feature combinations for ordinal classification is characterized using rank entropy-based uncertainty measures. A new feature evaluation index and corresponding feature selection algorithm are also presented. Numerical experiments demonstrate the effectiveness of the proposed model and feature selection method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Kumar Tiwari, Tanmoy Som
Summary: This study introduces a novel approach to enhance the prediction of anti-tubercular peptides by extracting sequence features, selecting optimal features, and utilizing different machine learning techniques. The proposed method outperforms previous methods and achieves high accuracy and sensitivity rates.
Article
Computer Science, Artificial Intelligence
Changzhong Wang, Yuhua Qian, Weiping Ding, Xiaodong Fan
Summary: This article proposes a novel criterion function for feature selection by redefining the concepts of fuzzy rough approximations using a class of irreflexive and symmetric fuzzy binary relations, and introducing the concept of inner product dependency to describe classification errors. Experimental results demonstrate the effectiveness of the proposed criterion function for datasets with a large overlap between different categories.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
S. Ramakrishnan, A. S. Muthanantha Murugavel
PATTERN ANALYSIS AND APPLICATIONS
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Loganathan Meenachi, Srinivasan Ramakrishnan
CURRENT MEDICAL IMAGING
(2020)
Article
Engineering, Electrical & Electronic
S. Ramakrishnan, S. Ponni Alias Sathya
IETE JOURNAL OF RESEARCH
(2020)
Article
Computer Science, Hardware & Architecture
S. Ramakrishnan, Subrat Kar, Dharmaraja Selvamuthu
Article
Computer Science, Artificial Intelligence
Sethuraman Ponni Alias Sathya, Srinivasan Ramakrishnan
IET IMAGE PROCESSING
(2020)
Article
Physics, Multidisciplinary
S. Nithya, S. Ramakrishnan
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2020)
Article
Computer Science, Information Systems
P. Sathiyamurthi, S. Ramakrishnan
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
L. Meenachi, S. Ramakrishnan
Article
Computer Science, Artificial Intelligence
S. Nithya, S. Ramakrishnan
Summary: This paper proposes a new texture classification method called wavelet domain majority coupled binary pattern, which achieves efficient image retrieval using wavelet transform and binary patterns, and demonstrates superior performance in experiments.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
P. Sathiyamurthi, S. Ramakrishnan
Summary: In this paper, a novel speech encryption algorithm based on a hybrid-hyper chaotic system is proposed. The algorithm improves the security level of speech communication models by utilizing a hybrid-hyper chaotic system instead of a normal chaotic system. The algorithm compresses the input speech signal using Discrete Cosine Transform (DCT) and permutes the compressed signal using a hybrid chaotic system designed using Zaslavsky and Zigzag maps. The substitution process involves generating a reference speech signal using Hidden Markov Model (HMM) speech synthesizer and permuting it with a hyper-chaotic system. The encryption signal is masked using a masking sequence obtained from the hyper-chaotic system. Various cryptographic metrics and analyses are conducted to evaluate the proposed algorithm, and the results demonstrate its high security and robust encryption and decryption quality.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Udayamoorthy Venkateshkumar, Srinivansan Ramakrishnan
JOURNAL OF ENGINEERING-JOE
(2020)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)
