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

The invention discloses a remote sensing image retrieval method based on feature selection and semi-supervised learning. In the method, an optimal color feature and an optimal textural feature are selected respectively by utilizing a clustering method according to a minimum description length criterion and an improved Davies-Bouldin index; and then an appropriate semi-supervised learning method is selected according to the binarization weight of the optimal color feature and the optimal textural feature for carrying out remote sensing image retrieval. Compared with the traditional remote sensing image retrieval method, the invention not only can greatly improve the retrieval quality, but also can reduce the calculate quantity in the retrieval process and improve the retrieval speed.

Remote sensing image retrieval method based on feature selection and semi-supervised learning

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

Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly the same accuracies on the target application. The experimental results also show that it is necessary to conduct feature selection, which can reduce the computation cost largely, while the accuracy remains the same or even better.

Shijin Li

Papers

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

Remote sensing image retrieval method based on feature selection and semi-supervised learning

A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

Comparative Study of Three Feature Selection Methods for Regional Land Cover Classification Using MODIS Data

A Fast Approach to Hyperspectral Band Selection Based on Time Series Analysis