Semi-Supervised Evolutionary Ensembles for Web Video Categorization

• Impact Factor:8.139
• DOI number:10.1016/j.knosys.2014.11.030
• Affiliation of Author(s):西南交通大学
• Journal:Knowledge-Based Systems
• Place of Publication:NETHERLANDS
• Key Words:Genetic algorithmSemantic similarityClustering ensembleSocial media miningVideo categorization
• Abstract: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.
• Co-author:YanYang,HongjunWang,MehtabAfzal
• First Author:Amjad Mahmood
• Indexed by:Academic papers
• Correspondence Author:TianruiLi
• Document Code:20150800547968
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 76
• Page Number:53-66
• ISSN No.:0950-7051
• Translation or Not:no
• Date of Publication:2014-12-15