Corpus ID: 212581488

Feature Subset Evaluation and Classification using Naive Bayes Classifier

@inproceedings{Keerthika2015FeatureSE,
  title={Feature Subset Evaluation and Classification using Naive Bayes Classifier},
  author={G. Keerthika},
  year={2015}
}
Feature Reduction is the reduction of features. Most of the intrusion detection approaches focused on feature selection issues such as irrelevancy, redundancy and length of detection process. These issues will degrade the performance of system. The performance of the system is improved by three feature selection methods involving correlation based feature selection, Gain Ratio and Information Gain. The threshold based Naive feature reduction algorithm is used to reduce the features. The reduced… Expand

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References

SHOWING 1-10 OF 32 REFERENCES
Fast Feature Selection for Naive Bayes Classification in Data Stream Mining
TLDR
Experimental results demonstrate that continuous feature selection for NB stream mining provides high levels of predictive performance and efficient computational methods for selecting relevant features for NB classification based on the sliding window method of stream mining are reported. Expand
Feature Selection: A literature Review
TLDR
The concepts of feature relevance, general procedures, evaluation criteria, and the characteristics of feature selection are introduced and guidelines are provided for user to select a feature selection algorithm without knowing the information of each algorithm. Expand
Efficient Feature Selection via Analysis of Relevance and Redundancy
TLDR
It is shown that feature relevance alone is insufficient for efficient feature selection of high-dimensional data, and a new framework is introduced that decouples relevance analysis and redundancy analysis. Expand
Toward optimal feature selection using ranking methods and classification algorithms
TLDR
It is shown that the selection of ranking methods could be important for classification accuracy, and six ranking methods that can be divided into two broad categories: statistical and entropy-based are considered. Expand
Effective Discretization and Hybrid feature selection using Naïve Bayesian classifier for Medical datamining
TLDR
This work demonstrates that the proposed algorithm using generative Naive Bayesian classifier on the average is more efficient than using discriminative models namely Logistic Regression and Support Vector Machine. Expand
Correlation-based Feature Selection for Machine Learning
TLDR
This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. Expand
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
TLDR
A novel concept, predominant correlation, is introduced, and a fast filter method is proposed which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis. Expand
Feature Selection for Classification
TLDR
This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications. Expand
An Effective Approach to Network Intrusion Detection System using Genetic Algorithm
TLDR
The purpose of this paper is to give a clear understanding of the use of Genetic Algorithm in IDS. Expand
Online Feature Selection and Its Applications
TLDR
This article investigates the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features, and presents novel algorithms to solve each of the two problems. Expand
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