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A hybrid feature selection method for text classification using a feature-correlation-based genetic algorithm

Soft Computing 2024年第23期28卷 13567-13593页

作者： Farek, Lazhar Benaidja, Amira Computer Science Department University of Guelma Guelma Algeria Computer Science Department University of Setif 1 Setif Algeria Laboratory of Vision and Artificial Intelligence - LAVIA Larbi Tebessi University Tebessa Algeria

This paper introduces a new hybrid method to address the issue of redundant and irrelevant features selected by filter-based methods for text classification. The method utilizes an enhanced genetic algorithm called "Feature Correlation-based Genetic Algorithm" (FC-GA). Initially, a feature subset with the highest classification accuracy is selected by a filter-based method, which will be then used by the FC-GA to generate potential solutions by considering the correlation between features that have similar classification weights and avoiding useless random solutions. The encoding process involves assigning a value of 0 to features that provide a high degree of correlation with other features having almost the same classification information beyond a specified context, while features that are lowly correlated retain their initial code of 1. Through iterative optimization using crossover and mutation operators, the algorithm should remove redundant features that provide strong correlations and high redundancy, which could lead to improved classification performance at a lower computation cost. The aim of this study is to improve the efficiency of filter-based methods, incorporate feature correlation information into genetic algorithms, and utilize pre-optimized feature subsets to efficiently identify optimal solutions. To evaluate the effectiveness of the proposed method, SVM and NB classifiers are employed on six public datasets and compared to five well-known and effective filter-based methods. The results indicate that a significant portion (about 50%) of the features selected by reference filter-based methods are redundant. Eliminating those redundant features leads to a significant improvement in classification performance as measured by the micro-f1 measure. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Genetic algorithms

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Support vector machine with discriminative low-rank embedding

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CAAI Transactions on intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Dual Encoder-Decoder Shifted Window-Based Transformer Network for Polyp Segmentation with Self-Learning Approach

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第7期5卷 3456-3469页

作者： Lijin, P. Ullah, Mohib Vats, Anuja Cheikh, Faouzi Alaya Kumar, Santhosh Nair, Madhu S. Cochin University of Science and Technology Artificial Intelligence & Computer Vision Laboratory Department of Computer Science Kerala Kochi682022 India Norwegian University of Science and Technology Gjovik2815 Norway Norwegian University of Science and Technology Norwegian Colour and Visual Computing Laboratory Gjovik2815 Norway

According to WHO reports, cancer is the leading cause of death worldwide. The second most prevalent cause of cancer-related death in both men and women is colorectal cancer (CRC). One potential approach for reducing the severity of colon cancer is to utilize automatic segmentation and detection of colorectal polyps in colonoscopy videos. This technology can assist endoscopists in quickly identifying colorectal disease, leading to earlier intervention and better patient Quality of Life (QoL). In this article, we propose a self-supervised transformer based dual encoder-decoder architecture named P-SwinNet for polyps segmentation in colonoscopy images. The P-SwinNet adapts the dual encoder-decoder type of model to enhance the feature maps by sharing multiscale information from the encoder to the decoder. The proposed model uses multiple dilated convolutions to enlarge the field of view to gather more information without increasing the computational cost and the loss of spatial information. We also leverage a large-scale unlabeled dataset for training our model using the self-learning strategy of Barlow twins. Additionally, to capture the long-range dependencies in the data, we used a shift window-based approach that computes global attention. We extensively evaluate our model against state-of-the-art algorithms. The quantitative results show that the proposed P-SwinNet achieves a mean dice score of 0.87 and a mean Intersection over Union (IoU) of 0.82 on five datasets used in our study. This performance demonstrates a substantial advancement over existing similar works, highlighting the advantage and novelty of our proposed approach in the field of medical image segmentation. © 2020 IEEE.

关键词： Convolution

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Exploiting autoencoder’s weakness to generate pseudo anomalies

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Neural Computing and Applications 2024年第23期36卷 14075-14091页

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data are often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization 38

Towards Combating Frequency Simplicity-biased Learning for D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024 Neural information processing systems foundation. All rights reserved.

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Video Corpus Moment Retrieval with Query-specific Context Learning and Progressive Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5659-5670页

作者： Zhang, Long Song, Peipei Duan, Zhangling Wang, Shuo Chang, Xiaojun Yang, Xun School of Information Science and Technology Hefei230026 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Department of Computer Vision Abu Dhabi United Arab Emirates University of Science and Technology of China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition China

Video corpus moment retrieval (VCMR) aims to retrieve a moment from a large corpus of untrimmed videos corresponding to a given language query. However, existing methods often fall short due to their reliance on simple cross-modal attention mechanisms and one-stop localization, which fail to handle the complex multimodal information and large search space effectively. To address these challenges, we propose a novel VCMR method with Query-specific Context Learning and Progressive Localization (QCLPL). First, we construct query-specific multimodal contexts that capture complementary and consistent semantics across subtitles and frames, ensuring informative and efficient context building. We further introduce a semantic contrastive loss to refine these multimodal contexts, filtering out query-irrelevant information. Additionally, we introduce a progressive localization strategy that transforms the moment localization task into a two-stage process. By classifying frames into foreground and background regions, we present a simplified binary classification problem before boundary prediction, constrained by a region-aware loss. This progressive approach leverages region priors to improve subsequent moment localization. Extensive experiments on the TVR and DiDeMo datasets demonstrate that our method significantly outperforms existing approaches, setting a new state of the art for VCMR. © 1991-2012 IEEE.

关键词： Structured Query Language

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Activation Template Matching Loss for Explainable Face Recognition 17

Activation Template Matching Loss for Explainable Face Recog...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Lin, Huawei Liu, Haozhe Li, Qiufu Shen, Linlin Computer Vision Institute School of Computer Science and Softwre Enginnering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9798350345445

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss. © 2023 IEEE.

关键词： Face recognition

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LLaVA-Endo:a large language-and-vision assistant for gastrointestinal endoscopy

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Frontiers of computer science 2025年第4期19卷 121-123页

作者： Jieru YAO Xueran LI Qiang XIE Longfei HAN Yiwen JIA Nian LIU Dingwen ZHANG Junwei HAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China AHU-IAI AI Joint Laboratory Anhui UniversityHefei 230039China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China School of Computer and Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China Department of Gastroenterology The Third Affiliated Hospital of Anhui Medical University(Hefei First People’s Hospital)Hefei 230061China The Computer Vision Department Mohamed Bin Zayed University of Artificial IntelligenceMasdarAbu Dhabi 200120United Arab Emirates Xijing Hospital The Fourth Military Medical UniversityXi’an 710032China

1 Introduction Endoscopy plays a crucial role in the diagnoses and treatment of gastrointestinal(GI)diseases[1],as it helps to identify abnormalities,classify lesion,and determine treatment *** GI endoscopic examinations,physicians may encounter practical hindrances,i.e.,fatigue,stress,or limited experience,which can lead to erroneous *** intelligence(AI)-assisted GI endoscopy technology has emerged to address these limitations[2].

关键词： stress giendoscopy fatigue identify abnormalitiesclassify lesionidentification artificialintelligence

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PLIP: Language-Image Pre-training for Person Representation Learning 38

PLIP: Language-Image Pre-training for Person Representation ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI United States Department of Computer Vision Baidu Inc. China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2024 Neural information processing systems foundation. All rights reserved.

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