International Journal of Mobile Computing and Multimedia Communications 

About: International Journal of Mobile Computing and Multimedia Communications is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Computer science & Mobile computing. It has an ISSN identifier of 1937-9412. Over the lifetime, 243 publications have been published receiving 1536 citations.

A Strategy on Selecting Performance Metrics for Classifier Evaluation

Abstract: The evaluation of classifiers' performances plays a critical role in construction and selection of classification model. Although many performance metrics have been proposed in machine learning community, no general guidelines are available among practitioners regarding which metric to be selected for evaluating a classifier's performance. In this paper, we attempt to provide practitioners with a strategy on selecting performance metrics for classifier evaluation. Firstly, the authors investigate seven widely used performance metrics, namely classification accuracy, F-measure, kappa statistic, root mean square error, mean absolute error, the area under the receiver operating curve, and the area under the precision-recall curve. Secondly, the authors resort to using Pearson linear correlation and Spearman rank correlation to analyses the potential relationship among these seven metrics. Experimental results show that these commonly used metrics can be divided into three groups, and all metrics within a given group are highly correlated but less correlated with metrics from different groups.

Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

Abstract: In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection CFS and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP'99 dataset were also tested. Experiments show that the authors' method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.

Application of WMN-SA Simulation System for Node Placement in Wireless Mesh Networks: A Case Study for a Realistic Scenario

Abstract: One of the key advantages of Wireless Mesh Networks WMNs is their importance for providing cost-efficient broadband connectivity. In WMNs, there are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, the authors consider the router node placement problem in WMNs. The objective is to find the optimal distribution of router nodes in order to provide the best network connectivity the maximal number of connected routers and coverage maximal number of covered clients. The authors apply their proposed WMN-SA simulation system in a realistic scenario of the distribution of mesh clients considering Itoshima City, Fukuoka Prefecture, Japan. From simulation results, they found many insights that can be very important for real deployment of WMNs.

Quality of Experience Models for Multimedia Streaming

Abstract: Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience QoE is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.