MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification

Shen, Xiang-Jun; LuKou,; Mehta, Sumet; ZhangJianming,; LiuWeifeng,; FanJianping,; ZhaZhengjun,

doi:10.1145/3457217

Cited by 8 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed methodology uses SHAP (Shapley Additive exPlanations) [18] to interpret framework prediction. A global interpreter SHAP is used over LIME [22], to interpret the effect of the single feature on the target variable. SHAP framework utilizes various explainability methods for better interpretation of model prediction.…”

Section: Methodsmentioning

confidence: 99%

SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

et al. 2023

View full text Add to dashboard Cite

Deep learning has significantly aided current advancements in artificial intelligence. Deep learning techniques have significantly outperformed more than typical machine learning approaches, in various fields like Computer Vision, Natural Language Processing (NLP), Robotics Science, and Human-Computer Interaction (HCI). Deep learning models are ineffective in outlining their fundamental mechanism. That's the reason the deep learning model mainly consider as Black-Box. To establish confidence and responsibility, deep learning applications need to explain the model's decision in addition to the prediction of results. The explainable AI (XAI) research has created methods that offer these interpretations for already trained neural networks. It's highly recommended for computer vision tasks relevant to medical science, defense system, and many more. The proposed study is associated with XAI for Sign Language Recognition. The methodology uses an attention-based ensemble learning approach to create a prediction model more accurate. The proposed methodology used ResNet50 with the Self Attention model to design ensemble learning architecture. The proposed ensemble learning approach has achieved remarkable accuracy at 98.20%. In interpreting ensemble learning prediction, the author has proposed SignExplainer to explain the relevancy (in percentage) of predicted results. SignExplainer has illustrated excellent results, compared to other conventional Explainable AI models reported in state of the art.

show abstract

Section: Methodsmentioning

confidence: 99%

SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In computer vision, ensemble learning has been successfully applied to a wide range of tasks, such as image classification [58], object detection [56], [56], [59], image segmentation [57], human activity recognition [60], crack detection and visual artifacts [61]- [63], wear identification [64], and facial recognition [55], [65].…”

Section: A Ensemble Learning In Computer Visionmentioning

confidence: 99%

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

Abimannan,

El-Alfy,

Chang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models.

show abstract

“…In previous studies on MKL, various approaches have been used to select base learning kernels. Some approaches (Han et al 2018;Shen et al 2021) studied predefined kernels from commonly used kernels (e.g., linear, Gaussian, and Polynomial) with different parameters. Chamakura and Saha (Chamakura and Saha 2022) used linear kernel, Intersection kernel, and Chi-squared kernel as base learning kernels.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Randomized Kernel Classification via Nonconvex Optimization

Ding,

Yang

2024

AAAI

View full text Add to dashboard Cite

Multi kernel learning (MKL) is a representative supervised multi-view learning method widely applied in multi-modal and multi-view applications. MKL aims to classify data by integrating complementary information from predefined kernels. Although existing MKL methods achieve promising performance, they fail to consider the tradeoff between diversity and classification accuracy of kernels, preventing further improvement of classification performance. In this paper, we tackle this problem by generating a number of high-quality base learning kernels and selecting a kernel subset with maximum pairwise diversity and minimum generalization errors. We first formulate this idea as a nonconvex quadratic integer programming problem. Then we transform this nonconvex problem into a convex optimization problem and prove it is equivalent to a semidefinite relaxation problem, which a semidefinite-based branch-and-bound algorithm can quickly solve. Experimental results on the real-world datasets demonstrate the superiority of the proposed method. The results also show that our method works for the support vector machine (SVM) classifier and other state-of-the-art kernel classifiers.

show abstract

MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification

Cited by 8 publications

References 46 publications

SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

Multi-View Randomized Kernel Classification via Nonconvex Optimization

Contact Info

Product

Resources

About