Relevant feature identification has become an essential task to apply data mining algorithms effectively in real-world scenarios. Therefore, many feature selection methods have been proposed to obtain the relevant feature or feature subsets in the literature to achieve their objectives of classification and clustering. This paper introduces the concepts of feature relevance, general procedures, evaluation criteria, and the characteristics of feature selection. A comprehensive overview, categorization, and comparison of existing feature selection methods are also done, and the guidelines are also provided for user to select a feature selection algorithm without knowing the information of each algorithm. We conclude this work with real world applications, challenges, and future research directions of feature selection.

https://www.cc.gatech.edu/~hic/CS7616/Papers/Kumar-Minz-2014.pdf

Feature Selection: A literature Review

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.

An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine

An automated detection and classification of citrus plant diseases using image processing techniques: A review

A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral information of ground objects. In this article, we review current hyperspectral band selection methods, which can be classified into six main categories: ranking based, searching based, clustering based, sparsity based, embedding-learning based, and hybrid-scheme based. With two widely used hyperspectral data sets, we illustrate the classification performances of several popular band selection methods. The challenges and research directions of hyperspectral band selection are also discussed.

Hyperspectral Band Selection: A Review

A new local search based hybrid genetic algorithm for feature selection

With the development of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, in which to select a minimal and effective subset from a mass of bands is the key issue. This paper put forward a novel band selection strategy based on conditional mutual information between adjacent bands and branch and bound algorithm for the high correlation between the bands. In addition, genetic algorithm and support vector machine are employed to search for the best band combination. Experimental results on two benchmark data set have shown that this approach is competitive and robust.

A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

During the last three decades, the imaging satellite sensors have acquired huge quantities of remote sensing data. Content-based image retrieval is one of the effective and efficient techniques for utilizing those Earth observation data sources. In this paper, a novel remote sensing image retrieval approach, which is based on feature selection and semi-supervised learning, is proposed. The new method includes four steps. Firstly, clustering is employed to select features and the number of clusters is determined automatically by using the MDL criterion; Secondly, according to an improved clustering validity index, we select the optimal features which can describe the retrieval objectives efficiently; Thirdly, the weights of the selected features are dynamically determined; and finally, an appropriate semi-supervised learning scheme is adaptively selected and image retrieval is thus conducted. Experimental results show that, the proposed approach can achieve comparable performance to the relevance feedback method, while ours need no human interaction.

Jiali Zhu

Papers

An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine

A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

Clustering-based feature selection for content based remote sensing image retrieval