Article
Physics, Multidisciplinary
Shouta Sugahara, Maomi Ueno
Summary: Previous research has shown that the classification accuracies of Bayesian networks obtained by maximizing the conditional log likelihood were higher than those obtained by maximizing the marginal likelihood. However, in cases with small sample sizes and a class variable with multiple parents, the accuracies of exact learning with ML were significantly lower. Introducing an exact learning augmented naive Bayes classifier improved the situation and guaranteed similar class posterior estimation as exact learning Bayesian networks.
Article
Computer Science, Artificial Intelligence
Anderson Ara, Francisco Louzada
Summary: The main goal of this paper is to introduce a new procedure for a naive Bayes classifier, called alpha skew Gaussian naive Bayes (ASGNB), which utilizes a flexible generalization of the Gaussian distribution on continuous variables. ASGNB is capable of handling asymmetry or bimodal behavior in the data and outperforms other traditional classification methods in terms of predictive performance.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Computer Science, Artificial Intelligence
Hongpo Zhang, Ning Cheng, Yang Zhang, Zhanbo Li
Summary: Label flipping attack is a poisoning attack that reduces the classification performance of a model by flipping the labels of training samples. Naive Bayes algorithm demonstrates good robustness in handling issues like document classification and spam filtering. The proposed label flipping attacks effectively reduce the accuracy of various classification models.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Operations Research & Management Science
Peter L. Jackson
Summary: This paragraph discusses the reformulation of the support vector problem as an application of Bayes' classifiers, emphasizing its simplicity and applicability to multi-class classification problems.
OPERATIONS RESEARCH LETTERS
(2022)
Article
Engineering, Environmental
Azimah Ismail, Hafizan Juahir, Saiful Bahri Mohamed, Mohd Ekhwan Toriman, Azlina Md Kassim, Sharifuddin Md Zain, Hadieh Monajemi, Wan Kamaruzaman Wan Ahmad, Munirah Abdul Zali, Ananthy Retnam, Mohd Zaki Mohd Taib, Mazlin Mokhtar, Siti Nor Fazillah Abdullah
Summary: This study focuses on exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in Peninsular Malaysia through Support Vector Machines based on Kernel-Radial Basis Function approach. The results show efficient and reliable oil sample classification, with high precision classification achieved for different oil spill types from sample fingerprinting. The study also highlights the perfect separability of oil type classification and the successful prediction of support vectors for certain oil types.
WATER SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Ahmad Khajenezhad, Mohammad Ali Bashiri, Hamid Beigy
Summary: The proposed nested Log-Poly model provides an accurate density estimation in a distributed setting by transferring small-sized statistics from client nodes to a central node for estimation. It is particularly suitable for one-dimensional density estimations and is a good choice for naive Bayes classifier.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Sasan H. Alizadeh, Alireza Hediehloo, Nima Shiri Harzevili
Summary: Naive Bayes classifier is known for its simplicity and performance, but many generalizations compromise simplicity and lead to more complex models. The Multi Independent Latent Component Naive Bayes Classifier uses latent variables to maintain the model structure while reducing complexities in classification and learning. Experimental results show that it outperforms state-of-the-art classifiers in terms of AUC and ACC.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Huan Zhang, Liangxiao Jiang
Summary: Naive Bayes (NB) is a simple, efficient, and effective data mining algorithm. However, its performance is limited by the unrealistic attribute conditional independence assumption and unreliable conditional probability estimation. This study proposes a novel model called fine tuned attribute weighted NB (FTAWNB), which combines fine tuning with attribute weighting to enhance NB's performance by improving both the attribute conditional independence assumption and conditional probability estimation.
Article
Computer Science, Artificial Intelligence
Huan Zhang, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li
Summary: In this study, a new model called multi-view attribute weighted naive Bayes (MAWNB) is proposed to portray data characteristics more comprehensively. By constructing two label views from raw attributes and optimizing attribute weights, MAWNB can predict class labels for test instances with high accuracy. Extensive experiments demonstrate the superiority of MAWNB compared to NB and other state-of-the-art competitors.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Shihe Wang, Jianfeng Ren, Ruibin Bai
Summary: Recently, improved naive Bayes methods, including regularized naive Bayes (RNB), have been developed to enhance discrimination capabilities. However, these methods often result in significant information loss due to inadequate data discretization. To address this issue, we propose a semi-supervised adaptive discriminative discretization framework that utilizes both labeled and unlabeled data to better estimate the data distribution. Our proposed method, called RNB+, shows superior performance compared to state-of-the-art NB classifiers by significantly reducing information loss and improving discrimination power.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Shengfeng Gan, Shiqi Shao, Long Chen, Liangjun Yu, Liangxiao Jiang
Summary: The paper introduces a single model called hidden MNB (HMNB), which creates a hidden parent for each feature to synthesize the influences of all other qualified features by adapting the method of hidden NB (HNB). A simple but effective learning algorithm is proposed and applied to text classification datasets, validating the effectiveness of HMNB in text classification.
Article
Engineering, Biomedical
Chayashree Patgiri, Amrita Ganguly
Summary: Detection of anomalous cells in blood diseases is crucial, and automatic recognition with robust segmentation and classification methods can improve efficiency. A novel hybrid segmentation method using features extracted from cells for training and testing classifiers shows potential for high performance in classifying normal and sickle cells.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Chemistry, Analytical
Mingu Kang, Siho Shin, Gengjia Zhang, Jaehyo Jung, Youn Tae Kim
Summary: The study presented a method for classifying ECG data into different emotional states based on stress levels, improving accuracy by calculating specific ECG features, with an average accuracy of 97.6%. The proposed model increased accuracy by 8.7% compared to previous algorithms, offering a tool for effectively managing mental state by quantifying stress signals experienced by individuals.
Article
Computer Science, Information Systems
Huan Zhang, Liangxiao Jiang, Chaoqun Li
Summary: In this study, a novel model called A(2)WNB is proposed to address the limitation of the attribute conditional independence assumption in naive Bayes algorithm. By discovering and utilizing latent attributes beyond the original attribute space, as well as optimizing attribute weights to reduce attribute redundancy, the A(2)WNB model demonstrates superior performance in classification tasks.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Alaaeddine Yousfi, Anind K. Dey, Rajaa Saidi, Jin-Hyuk Hong
COMPUTERS IN INDUSTRY
(2015)
Article
Computer Science, Artificial Intelligence
Jun-Ki Min, Jin-Hyuk Hong, Sung-Bae Cho
EXPERT SYSTEMS WITH APPLICATIONS
(2015)
Article
Computer Science, Information Systems
Hristijan Gjoreski, Simon Kozina, Matjaz Gams, Mitja Lustrek, Juan Antonio Alvarez-Garcia, Jin-Hyuk Hong, Julian Ramos, Anind K. Dey, Maurizio Bocca, Neal Patwari
IEEE PERVASIVE COMPUTING
(2015)
Article
Computer Science, Artificial Intelligence
Jin-Hyuk Hong, Julian Ramos, Anind K. Dey
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2016)
Article
Engineering, Electrical & Electronic
Selim Ickin, Katarzyna Wac, Markus Fiedler, Lucjan Janowski, Jin-Hyuk Hong, Anind K. Dey
IEEE COMMUNICATIONS MAGAZINE
(2012)
Article
Computer Science, Cybernetics
Jin-Hyuk Hong, Anind K. Dey
INTERACTING WITH COMPUTERS
(2015)
Article
Green & Sustainable Science & Technology
Dinuka Ravimal, Hanul Kim, Daegwon Koh, Jin Hyuk Hong, Sun-Kyu Lee
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY
(2020)
Article
Chemistry, Multidisciplinary
Yeo-Gyeong Noh, Jin-Hyuk Hong
Summary: Researchers examined the role of chatbots in history education in museums and how they impact visitors' experiences. The findings demonstrate that embodiment and reflection significantly enhance the museum experience.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Albert Deok-Young Yang, Yeo-Gyeong Noh, Jin-Hyuk Hong
Summary: By validating topic recommendation strategies of system-initiative QA agents, this paper examines how different recommendations influence users' experience in museums. The study with 50 participants shows that providing recommendations on various subjects expands their interest, supports longer conversations, and increases willingness to use QA agents in the future.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Ji Hyun Yi, Songei Kim, Yeo-Gyeong Noh, Subin Ok, Jin-Hyuk Hong
Summary: The study found that sign language interpreters should be clearly visible on the screen without a background, as DHH participants preferred it that way. Providing sign language interpretation and subtitles together helps DHH participants understand content more quickly and accurately. Reference videos were considered less important compared to sign language interpreters and subtitles for DHH participants.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
JaeYoung Moon, YouJin Choi, TaeHwa Park, JunDoo Choi, Jin-Hyuk Hong, Kyung-Joong Kim
Summary: Game developers have used dynamic difficulty adjustment (DDA) to improve players' game experience. To enhance the game experience further, they have designed game AI opponents that consider players' affective states. These opponents utilize the Monte-Carlo tree search (MCTS) algorithm and machine learning models to estimate the states, resulting in a better game experience.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Cybernetics
In-Taek Jung, Sooyeon Ahn, JuChan Seo, Jin-Hyuk Hong
Summary: Gesture data collection in a controlled lab environment may not sufficiently capture the variability of gestures, while crowdsourcing proves to be effective in collecting more representative and variable data. Compared to the lab environment, crowdsourcing improves recognition performance by 8.98% and increases variability in various gesture features. The study also explores the efficacy of gesture collection methodologies and memorization paradigms.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Proceedings Paper
Computer Science, Information Systems
JooYeong Kim, SooYeon Ahn, Jin-Hyuk Hong
Summary: This paper proposes an audio-visualized caption system to enhance the understanding of video content for deaf and hard-of-hearing individuals, particularly in conveying paralinguistic cues.
PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023)
(2023)
Article
Computer Science, Artificial Intelligence
Youjin Choi, JooYeong Kim, Jin-Hyuk Hong
Summary: This study proposes an objective model for measuring immersion in video watching by utilizing viewers' gaze behavior. The results of the laboratory study indicate that certain gaze features are highly indicative of viewers' immersion state, and machine learning models based on these features are able to effectively detect the immersion state of viewers. Additionally, post-hoc interviews confirm the applicability of this method for measuring immersion in the middle of video watching.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Information Systems
Ji Hyun Yi, Woojin Kang, Song-Ei Kim, Doyun Park, Jin-Hyuk Hong
Summary: The Smart Culture Lens developed in this study is an application that utilizes the visual element classification system of ceramics and AI technology, allowing users to analyze ceramic photos and search for similar artworks. The development process involved defining criteria, collecting data, training object detection models, and ultimately creating a mobile application to explore cultural heritage artifacts. The integration of AI technology into cultural heritage exploration provides a new perspective for intuitive artifact exploration.
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)
