Journal ArticleDOI
An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine
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TLDR
A hybrid feature selection strategy based on genetic algorithm and support vector machine (GA-SVM) formed a wrapper to search for the best combination of bands with higher classification accuracy, which reduced the computational cost of the genetic algorithm.Abstract:
With the development and popularization of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, such as target detection and land cover investigation. It is a very challenging issue of urgent importance to select a minimal and effective subset from those mass of bands. This paper proposed a hybrid feature selection strategy based on genetic algorithm and support vector machine (GA-SVM), which formed a wrapper to search for the best combination of bands with higher classification accuracy. In addition, band grouping based on conditional mutual information between adjacent bands was utilized to counter for the high correlation between the bands and further reduced the computational cost of the genetic algorithm. During the post-processing phase, the branch and bound algorithm was employed to filter out those irrelevant band groups. Experimental results on two benchmark data sets have shown that the proposed approach is very competitive and effective.read more
Citations
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Journal ArticleDOI
Feature Selection: A literature Review
Vipin Kumar,Sonajharia Minz +1 more
TL;DR: 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.
Journal ArticleDOI
An automated detection and classification of citrus plant diseases using image processing techniques: A review
Zahid Iqbal,Muhammad Attique Khan,Muhammad Attique Khan,Muhammad Sharif,Jamal Hussain Shah,Muhammad Habib ur Rehman,Kashif Javed +6 more
TL;DR: A survey on the different methods relevant to citrus plants leaves diseases detection and the classification reveals that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy and new tools are needed to fully automate the detection and Classification processes.
Journal ArticleDOI
Hyperspectral Band Selection: A Review
Weiwei Sun,Qian Du +1 more
TL;DR: Current hyperspectral band selection methods are reviewed, which can be classified into six main categories: ranking based, searching based, clustering based, sparsity based, embedding-learning based, embedded learning based, and hybrid-scheme based.
Journal ArticleDOI
A new local search based hybrid genetic algorithm for feature selection
TL;DR: A new hybrid genetic algorithm (HGA) for feature selection (FS), called HGAFS, which produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.
Journal ArticleDOI
Genetic algorithms in feature and instance selection
TL;DR: It is demonstrated that performing feature selection first and instance selection second is the optimal solution for data preprocessing in data mining.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
Selection of relevant features and examples in machine
Avrim L. Bluma,Pat Langley +1 more
TL;DR: A survey of machine learning methods for handling data sets containing large amounts of irrelevant information can be found in this article, where the authors focus on two key issues: selecting relevant features and selecting relevant examples.