An Efficient Classification Approach for Novel Class Detection by Evolving Feature Datastreams
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 6, Issue 3
Abstract
Data stream classification has been an extensively studied research problem in recent years. data stream classification requires efficient and effective techniques that are significantly different from static data classification techniques because of its dynamic nature. Existing system faces major challenges in the methods namely feature-evolution, infinite length, concept-drift and concept-evolution. To address and overcome the problems in these techniques an ensemble classification framework is proposed where each classifier is equipped with a novel class detector and addresses concept-driftand concept-evolution. It also addresses feature-evolution by a technique called a feature set homogenization technique. It also enhances the novel class detection module by making it more adaptive to the evolving stream. And make this enable to detect more than one novel class at a time. But all of methods doesn’t support for the detection of the outlier class by using clustering methods. To overcome this problem Outlier Detection has been proposed which is a very important research problem in data mining. These outliers are detected efficiently by using clustering algorithms. CLARANS clustering algorithm is proposed for detecting outliers in the class. The outlier class is detected before the novel class detection algorithm is performed. The best outlier in the class can be found and then it is applied to MCM (multiclass miner) in data streams. It is more adaptive technique to the evolving stream and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.
Authors and Affiliations
R. Shree alaguvidhya , C. Yamini
Keywords
Related Articles
- EP ID EP146842
- DOI -
- Views 119
- Downloads 0
Effect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction
Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in d...
A Presumption Mold of Visual Cryptography Design with Dynamic Groups
Visual cryptography is a secret sharing scheme where an image is encoded into transparencies. The Secret information can be revealed from the encoded image only when the correct set of images is given as an input and if...
Performance Analysis of SEP and LEACH for Heterogeneous Wireless Sensor Networks
While wireless sensor networks are increasingly equipped to handle more complicated functions, these battery powered sensors which used in network processing, use their constrained energy to enhance the lifetime of the n...
Analysis of Email Fraud Detection Using WEKA Tool
Data mining is also being useful to give solutions for invasion finding and auditing. While data mining has several applications in protection, there are also serious privacy fears. Because of email mining, even inexperi...
Microblogging Service to Report about Earthquake
Data Mining is the extraction of hidden predictive information from large Database set. The huge amount of data is a key resource to be processed and analyzed for knowledge extraction. Volcanic action Reporting system is...