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All Journal ComEngApp : Computer Engineering and Applications Journal
Mohammad Masoud Javidi
University of Kerman, Iran
Computer Science & IT Engineering

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Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies Mohammad Masoud Javidi; Fatemeh Shamsezat
Computer Engineering and Applications Journal Vol 4 No 1 (2015)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (698.335 KB) | DOI: 10.18495/comengapp.v4i1.109

Abstract

Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life. Such as, a movie can be categorized as action, crime, and thriller. Most algorithms on multi-label classification learning are designed for balanced data and don’t work well on imbalanced data. On the other hand, in real applications, most datasets are imbalanced. Therefore, we focused to improve multi-label classification performance on imbalanced datasets. In this paper, a state-of-the-art multi-label classification algorithm, which called IBLR_ML, is employed. This algorithm is produced from combination of k-nearest neighbor and logistic regression algorithms. Logistic regression part of this algorithm is combined with two ensemble learning algorithms, Bagging and Boosting. My approach is called IB-ELR. In this paper, for the first time, the ensemble bagging method whit stable learning as the base learner and imbalanced data sets as the training data is examined. Finally, to evaluate the proposed methods; they are implemented in JAVA language. Experimental results show the effectiveness of proposed methods. Keywords: Multi-label classification, Imbalanced data set, Ensemble learning, Stable algorithm, Logistic regression, Bagging, Boosting
Game Theory Approaches in Taxonomy of Intrusion Detection for MANETs Mohammad Masoud Javidi; Laya Aliahmadipour
Computer Engineering and Applications Journal Vol 4 No 1 (2015)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.062 KB) | DOI: 10.18495/comengapp.v4i1.111

Abstract

MANETs are self configuring networks that are formed by a set of wireless mobile nodes and have no fixed network infrastructure nor administrative support. Since transmission range of wireless network interfaces is limited, forwarding hosts may be needed. Each node in a wireless ad hoc network functions is as both a host and a router. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions so, security is a critical issue. Network security is usually provided in the three phases: intrusion prevention, intrusion detection and intrusion tolerance phase. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theory approaches to address the network security issues. This paper reviews some existing game theory solutions which are designed to enhance network security in the intrusion detection phase. Keywords: Mobile Ad hoc Network (MANET), Intrusion detection system (IDS), Cluster head, host based, Game theory.
Co-Authors Fatemeh Shamsezat Laya Aliahmadipour