ABSTRACT:

Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature elimination (CDFE), for binary data sets. Our theoretical analysis shows that CDFE computes the weights, used for feature ranking, more efficiently as compared to the mutual information measure. Effectively, rankings obtained from both the two criteria approximate each other. CDFE uses a filtrapper approach to select a final subset. For data sets having hundreds of thousands of features, feature selection with FR algorithms is simple and computationally efficient but redundant information may not be removed. On the other hand, FSS algorithms analyze the data for redundancies but may become computationally impractical on high-dimensional data sets. We address these problems by combining FR and FSS methods in the form of a two-stage feature selection algorithm. When introduced as a preprocessing step to the FSS algorithms, CDFE not only presents them with a feature subset, good in terms of classification, but also relieves them from heavy computations. Two FSS algorithms are employed in the second stage to test the two-stage feature selection idea. We carry out experiments with two different classifiers (naive Bayes' and kernel ridge regression) on three different real-life data sets (NOVA, HIVA, and GINA) of the ”Agnostic Learning versus Prior Knowledge” challenge. As a stand-alone method, CDFE shows up to about 92 percent reduction in the feature set size. When combined with the FSS algorithms in two-stages, CDFE significantly improves their classification accuracy and exhibits up to 97 percent reduction in the feature set size. We also compared CDFE against the winning entries of the challenge an-
found that it outperforms the best results on NOVA and HIVA while obtaining a third position in case of GINA.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection - 2018ABSTRACT:The tax provides an important role for the contributions of the economy and development of a rustic. The improvements
PROJECT TITLE :Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018ABSTRACT:With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although
PROJECT TITLE :Automatic Feature Selection Technique for Next Generation Self-Organizing Networks - 2018ABSTRACT:Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :Feature Map Quality Score Estimation Through Regression - 2018ABSTRACT:Understanding the visual quality of a feature map plays a important role in many active vision applications. Previous works mostly rely on object-level

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry