Ensemble learning is a significantly important research area for classification in machine learning. Ensemble approaches achieve enhanced performance by combining individual classifiers or training models and utilizing their strengths. Specifically, combining the outputs of several classifiers and models via averaging may reduce the risk of selecting a poorly performing classifier or model, and robust the overall accuracy. Thus, ensemble learning algorithms have been successfully applied to decision making, biomedical, financial, remote sensing, genomic, oceanographic, chemical or other types of data analysis and classification problems.
In recent years, with the increasing popularity of electro-optical sensors, such as visual and infrared cameras; as well as computer/device’s Central and Graphics Processing Units (CPU/GPU) and strong computing capability, deep learning and convolutional neural network have grown rapidly. Among many applications, the computer vision and intelligent video-based systems are particularly important owing to its effectiveness in providing interpretable visual information as follows: intelligent surveillance, intelligent transportation system, face recognition, person re-identification, anomaly detection, image segmentation, video tracking, and intelligent transportation systems.
In this book, we introduce not only the fundamental aspects of ensemble learning for visual applications, but also time-series noise reduction and natural language processing. In addition, we review conditions under which ensemble learning used in visual systems and deep learning may be more beneficial than a single model or classifier. We have also reviewed some of the more popular areas where ensemble learning has received significant attention in recent years in the machine learning and artificial intelligence community. The benefits of ensemble learning in automated decision-making applications, data/sensors fusion, imbalanced datasets, and few-shot learning, have been discovered by computational intelligence community. Finally, we look at current and future research directions for novel applications with ensemble learning.
Please kindly know that we welcome any subject about the theory, application, or implementation with ensemble learning. Please see below for the deadlines and other information for this title.
Abstract Deadline: 2/15/2020
Notification of acceptance for abstract: 2/29/2020
Chapter Deadline: 4/15/2020
Notification of acceptance for chapter: 5/31/2020
Authors must ensure that you carefully read the guide for authors before submitting your papers. The guide for authors and link for submission is available on the following “Important Note”.
For any inquiry or question regarding this special issue, authors may contact directly via email to Yi-Tung (Andy) Chan at eadown92@gmail.com, ytc106@mail.cna.edu.tw.
Important Note:
1. The archived packet (RIB-COB-R.zip) which includes the following required documents can be download using this link:
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Editor
Yi-Tung (Andy) Chan (Taiwan),
Assistant Professor, Department of Electrical Engineering, R.O.C. Naval Academy, Kaohsiung, Taiwan
Email: eadown92@gmail.com, ytc106@mail.cna.edu.tw
Sincerely,
Yi-Tung(Andy) Chan
Assistant Professor, Department of Electrical Engineering, , R.O.C. Naval Academy, Kaohsiung, Taiwan
Dept. of Electrical Engineering, No.669, Junxiao Rd., Zuoying Dist., Kaohsiung City 81345, Taiwan (R.O.C.)
Tel.: +886-7-5829681
E-mail: eadown92@gmail.com
