期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：9
页码：19044
DOI：10.15680/IJIRSET.2017.0609188
出版社：S&S Publications
摘要：This paper aims to propose the semi-supervised learning system that deals with huge and dynamic datain time and memory efficient manner. In today’s world the numbers of applications are available in information systemwhich deals with large amount of data in changing environment. In such environments, this data is available in the formonline streams. The memory and time limitations are the major aspects which need to be considered in online learningwhich are due to volume as well as speed of the data. In online streams, small quantity of information is labelled andhuge quantity of unlabelled information is accessible. Consequently, semi regulated learning is the best way to dealwith take in the information in order to decrease human efforts and as yet accomplishing better precision also theperformance of the system. This paper examined the concept of semi supervised learning. For learning the data streammax flow algorithm is used in this paper. The Max Flow/ Min Cut algorithm is applied for achieving the accuracy ofthe proposed system. The algorithm shows improved efficiency and enhancement in performance. To further enhancethe performance, Cosine Similarity and Feature Selection Techniques are used. The comparative study shows that theproposed system gives better performance in terms of time and memory efficiency than the old learning system. Thesystem is tested using KDD99 dataset for classification of network intrusion attacks. The system shows the higheraccuracy and improved performance results. Application: The proposed system gives solutions to various two classproblems. The applications such as network intrusion detection system which can be used to classify anomaly andnormal network, online audio background noise reduction of streams, traffic bottleneck identification system, imagesegmentation techniques where image can be segmented in background and foreground, 3D reconstruction and manymore can make use of the proposed system.
关键词：Machine learning; semi supervised learning; Max flow algorithm; data streams; cosine similarity; and;feature reduction.
