A new feature selection method based on a validity index of feature subset

Liu, Chuan; Wang, Wenyong; Zhao, Qiang; Shen, Xiaoming; Konan, Martin

doi:10.1016/j.patrec.2017.03.018

Cited by 69 publications

(33 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where HðyÞ and Hðy=XÞ show the entropy and conditional entropy of the tangled variable. Few researchers have addressed the problem 31 in MI-based feature selection (MIFS) method. Therefore, we used an attractive FS method recognized as CMIM method to reduce the redundancy between data attributes and class y as shown in Eq.…”

Section: Conditional Mutual Information Maximizationmentioning

confidence: 99%

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

Shukla

Singh

Vardhan

2019

Int. J. Comp. Intel. Appl.

View full text Add to dashboard Cite

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

show abstract

Section: Conditional Mutual Information Maximizationmentioning

confidence: 99%

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

Shukla

Singh

Vardhan

2019

Int. J. Comp. Intel. Appl.

View full text Add to dashboard Cite

show abstract

“…Liu et alproposed "a new statistical measure named as LW-index which could replace the expensive cross-validation scheme to evaluate the feature subset. Then, a new feature selection method, which is the combination of the proposed LW-index with Sequence Forward Search algorithm (SFS-LW), is presented in this paper" [8].…”

Section: Related Workmentioning

confidence: 99%

An Insight into Machine Learning Techniques for Predictive Analysis and Feature Selection

2019

IJITEE

View full text Add to dashboard Cite

Predictive analysis comprises a vast variety of statistical techniques like “machine learning”, “predictive modelling” and “data mining” and uses current and historical statistics to predict future outcomes. It is used in both business and educational domain with equal applicability.This paper aims to give an overview of the top work done so far in this field. We have briefed on classical as well as latest approaches (using“machine learning”) in predictive analysis. Main aspects like feature selection and algorithm selection along with corresponding application is explained. Some of the most quoted papers in this field along with their objectives are listed in a table. This paper can give a good heads up to whoever wants to know and use predictive analysis for his academic or business application.

show abstract

“…This will help to make the forward function more effective to implement and also reduce the amount of parameters in the network. But all these layers have the unidirectional connections with each and every neuron of the network [15] . So, these are networks are static in their behavior and the output of these networks is totally depends upon the present input pattern.…”

Section: Convolutional Neural Network: the Convolutionalmentioning

confidence: 99%

A Gist Warning of Sighting in Face Classification and Recognition

Jain*,

Kumar,

Joshi

2019

IJRTE

View full text Add to dashboard Cite

Face classification and recognition is the fastest growing, challenging area in real time applications. A large number of algorithms are there in the network to recognize the face. It is the important part of the biometric traits and it not only contributes to the theoretical insights but also to practical insights of many algorithms. Conversely, the first face recognition in the main reckons on a priori in a row of hurdle folks and might not free itself from human intervention. Until the looks of high-speed, betterquality computers, the face recognition methodology makes a big disintegrate through. Face recognition has been a quick growing, difficult and mesmerizing space in real time applications. Facial classifications and recognition becomes an interesting research topic. A large range of face classification and recognition algorithms are developed in last decades. In this paper a attempt is created to review a good vary of strategies used for face recognition expansively. This paper contributes a huge survey of varied face detection and feature extraction techniques. At the moment, there are loads of face classification and recognition techniques and algorithms found and developed round the world.

show abstract

A new feature selection method based on a validity index of feature subset

Cited by 69 publications

References 26 publications

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

An Insight into Machine Learning Techniques for Predictive Analysis and Feature Selection

A Gist Warning of Sighting in Face Classification and Recognition

Contact Info

Product

Resources

About