硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Semi-Supervised Evolutionary Ensembles for Web Video Categorization
影响因子：8.139
DOI码：10.1016/j.knosys.2014.11.030
所属单位：西南交通大学
发表刊物：Knowledge-Based Systems
刊物所在地：NETHERLANDS
关键字：Genetic algorithmSemantic similarityClustering ensembleSocial media miningVideo categorization
摘要：Evolutionary Algorithms (EA) have been developing rapidly as a powerful and general learning approach which has been used successfully to find a reasonable solution for data mining and knowledge discovery. Genetic algorithm (GA) is a kind of mainstream EA paradigm with a purpose of developing solutions for optimization problems. Clustering ensembles have emerged as an outstanding algorithm in machine learning to leverage the consensus across multiple clustering solutions and combines their predictions into a single solution with improved robustness, stability and accuracy. Multimedia advancement and popularity of the social Web has collectively provided an easy way to generate bulk of videos. Categorization of such Web videos has become a hot research challenge. In this paper, we propose a Semi-supervised Evolutionary Ensemble (SS-EE) framework for social media mining, e.g., Web Video Categorization (WVC), using their low cost textual features, intrinsic relations and extrinsic Web support. The contributions of this research work are as follows. First, we extend the traditional Vector Space Model (VSM) to Semantic VSM (S-VSM) by considering the semantic similarity between the feature terms using Normalized Google Distance (NGD) approach. Second, we define a new distance measure, Triangular Similarity (TrS) between two Textual Feature Vectors (TFV) based on the frequencies of most relevant terms in each category. Third, we iterate the clustering ensemble process with the help of GA guided by a new measure, Pre-Paired Percentage (PPP), to be used as the fitness function during the genetic cycle. Fourth, in the key steps of the GA, crossover and mutation genetic operators, we define them by an intelligent mechanism of clustering ensemble. Fifth, in order to terminate the genetic cycle, we define another new measure, Clustering Quality (Cq), based on similarity matrix and clustering labels. Experiments on real world social-Web data (YouTube) have been performed to validate the SS-EE framework.
合写作者：YanYang,HongjunWang,MehtabAfzal
第一作者：Amjad Mahmood
论文类型：学术论文
通讯作者：TianruiLi
论文编号：20150800547968
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 76
页面范围：53-66
ISSN号：0950-7051
是否译文：否
发表时间：2014-12-15