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Online Feature Selection an d Its Applications
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Online Feature Selection an d Its Applications

Category : Software Engneering


Sub Category : JAVA


Project Code : ITJSW04


Project Abstract

Online Feature Selection and Its Applications

 

ABSTRACT:

 

Online feature selection is very important in any web application and its tasks are called ofs. This paper work is closely related to the studies of online learning and feature selection in detail. Below we discuss important related works in both areas. When the incoming training example is either misclassified or fall into the range of classification area. The PA algorithm is limited in that it only the first order information during the updating. This limitation has been addressed by the recently proposed confidence weighted online learning algorithms that show the second order information. Instead of the extensive investigation, most studies of online learning require the access to all the features of training instance

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In existing learning methodologies studies of online learning require accessing all the attributes/features of training instances.

Existing, service integrity is the most prevalent problem, which needs to be addressed no matter whether public or private data are processed by the mining system.

PROPOSED CONCEPT:

We propose new algorithms to solve both of the above OFS tasks. We validate their practical performance by conducting a variety set of experiments and analyze theoretical properties of the proposed algorithms.

Web Search logs record user activities on search engines, such as queries and clicks.

EXISTING ALGORITHM:-

FS algorithm.

PROPOSED ALGORITHM:-

Partial OFS input algorithm.

ALGORITHM DEFINITION:-

   Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features to be collect for our application.                                                

ALGORITHM DEFINITION:-

We apply our algorithm to solve real-world problems in text classification, computer vision, and bioinformatics. A common Technique behind many budget online learning algorithms is to remove the oldest support data when the maximum number of support data is reached, which however is not applicable to online feature selection.

DRAWBACKS:-

This aims to select a large and number of features for initial classification in an online learning fashion.

The encouraging results show that the Existing algorithms are less effective for feature selection tasks of online applications.

ADVANTAGES:-

This aims to select a small and fixed number of features for initial classification in an online learning fashion.

The encouraging results show that the proposed algorithms are more effective for feature selection tasks of online applications.

 
 
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