Review of Ensemble Based Classification Algorithms for Nonstationary and Imbalanced Data
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 1
Abstract
Learning data samples from a non-stationary distribution has been shown to be a very challenging problem in machine learning, because the joint probability distribution between the data and classes changes over time. Most real time problems as they change with time can suffer concept drift. For example, a recommender or advertising system, in which customer’s behavior may change depending on the time of the year, on the inflation and on new products made available. An additional challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the class distributions are balanced. The objective of this paper is to review the ensemble classification algorithms on the framework of non-stationary and imbalanced dataset, with focus on two-class problems. In addition, we develop a thorough comparison of these algorithms by the consideration of the most significant published approaches.
Authors and Affiliations
Meenakshi A. Thalor
Keywords
Related Articles
- EP ID EP147037
- DOI -
- Views 120
- Downloads 0
Image Based Authentication for Folder Security using Persuasive Cued Click-Points and SHA
Abstract: In this paper Image Based Authentication application is applied on folder’s (folders that are confidential to user) ,since the studies till date have shown that textual passwords are more prone that the...
An Energy Efficient Traffic Routing On Wake-Up Process in Wireless Sensor Network
Abstract: Wireless network consists of distributed sensor nodes to monitor the physical conditions and establish the traffic system. Every node in WSN sends and receives the packet, resulting in wastage of bandwidth.Howe...
A Novel identity based secure distributed data storage scheme
Abstract: Secure distributed data storage can shift the burden of maintaining a large number of files from the owner to proxy servers. Proxy servers can convert encrypted files for the owner to encrypted files for...
Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images
Classification is one of the most important task in application areas of artificial neural networks (ANN).Training neural networks is a complex task in the supervised learning field of research. The main diffi...
[u][/u]Effect of Different Mobility Patterns on AODV and OLSR using Different TCP variants
Abstract: In this paper the effect of TCP variant (new reno, tahoe and Sack) is analyzed by using karan’s and nagle theorem by moving nodes at different mobility models on OLSR and AODV protocol. In this nagle theorem is...