A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis

2020 ◽  
Vol 138 ◽  
pp. 321-328 ◽  
Author(s):  
Saúl Solorio-Fernández ◽  
José Fco. Martínez-Trinidad ◽  
J. Ariel Carrasco-Ochoa
Keyword(s):  
Information Theory ◽  
Feature Selection ◽  
Redundancy Analysis ◽  
Spectral Feature ◽  
Feature Selection Method ◽  
Selection Method ◽  
Mixed Data ◽  
Spectral Feature Selection

A new Unsupervised Spectral Feature Selection Method for mixed data: A filter approach

2017 ◽  
Vol 72 ◽  
pp. 314-326 ◽  
Author(s):  
Saúl Solorio-Fernández ◽  
José Fco. Martínez-Trinidad ◽  
J. Ariel Carrasco-Ochoa
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Feature Selection Method ◽  
Selection Method ◽  
Mixed Data ◽  
Filter Approach ◽  
Spectral Feature Selection

scholarly journals Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

2020 ◽  
Vol 12 (1) ◽  
pp. 113 ◽  
Author(s):  
Andrew Hennessy ◽  
Kenneth Clarke ◽  
Megan Lewis
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Feature Selection Method ◽  
Selection Method ◽  
Classification Model ◽  
Support Vector ◽  
Stepwise Discriminant Analysis ◽  
Frequency Variation ◽  
Spectral Feature Selection ◽  
Hyperspectral Classification

Hyperspectral sensing, measuring reflectance over visible to shortwave infrared wavelengths, has enabled the classification and mapping of vegetation at a range of taxonomic scales, often down to the species level. Classification with hyperspectral measurements, acquired by narrow band spectroradiometers or imaging sensors, has generally required some form of spectral feature selection to reduce the dimensionality of the data to a level suitable for the construction of a classification model. Despite the large number of hyperspectral plant classification studies, an in-depth review of feature selection methods and resultant waveband selections has not yet been performed. Here, we present a review of the last 22 years of hyperspectral vegetation classification literature that evaluates the overall waveband selection frequency, waveband selection frequency variation by taxonomic, structural, or functional group, and the influence of feature selection choice by comparing such methods as stepwise discriminant analysis (SDA), support vector machines (SVM), and random forests (RF). This review determined that all characteristics of hyperspectral plant studies influence the wavebands selected for classification. This includes the taxonomic, structural, and functional groups of the target samples, the methods, and scale at which hyperspectral measurements are recorded, as well as the feature selection method used. Furthermore, these influences do not appear to be consistent. Moreover, the considerable variability in waveband selection caused by the feature selectors effectively masks the analysis of any variability between studies related to plant groupings. Additionally, questions are raised about the suitability of SDA as a feature selection method, with it producing waveband selections at odds with the other feature selectors. Caution is recommended when choosing a feature selector for hyperspectral plant classification: We recommend multiple methods being performed. The resultant sets of selected spectral features can either be evaluated individually by multiple classification models or combined as an ensemble for evaluation by a single classifier. Additionally, we suggest caution when relying upon waveband recommendations from the literature to guide waveband selections or classifications for new plant discrimination applications, as such recommendations appear to be weakly generalizable between studies.

Feature Selection Method by Information Theory and Particle S warm Optimization

2009 ◽  
Vol 19 (2) ◽  
pp. 191-196
Author(s):  
Jae-Hoon Cho ◽  
Dae-Jong Lee ◽  
Chang-Kyu Song ◽  
Myung-Geun Chun
Keyword(s):  
Information Theory ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method

KERNEL ENTROPY-BASED UNSUPERVISED SPECTRAL FEATURE SELECTION

2012 ◽  
Vol 26 (05) ◽  
pp. 1260002 ◽  
Author(s):  
ZHIHONG ZHANG ◽  
EDWIN R. HANCOCK
Keyword(s):  
Feature Selection ◽  
Dimensional Space ◽  
Cluster Structure ◽  
Spectral Feature ◽  
Feature Selection Method ◽  
Data Transformation ◽  
Classification Performance ◽  
Feature Selection Technique ◽  
Face Datasets ◽  
Spectral Feature Selection

Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they neglect the correlations among features. In an attempt to overcome this problem, we develop a novel feature selection technique using the spectral data transformation and by using ℓ1-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for unsupervised feature selection. In the first step, we use kernel entropy component analysis (kECA) to transform the data into a lower-dimensional space so as to improve class separation. Second, we use ℓ1-norm regularization to select the features that best align with the data embedding resulting from kECA. The advantage of kECA is that dimensionality reducing data transformation maximally preserves entropy estimates for the input data whilst also best preserving the cluster structure of the data. Using ℓ1-norm regularization, we cast feature discriminant analysis into a regression framework which accommodates the correlations among features. As a result, we can evaluate joint feature combinations, rather than being confined to consider them individually. Experimental results demonstrate the effectiveness of our feature selection method on a number of standard face datasets.

Improvement of feature selection method in spam filtering

2009 ◽  
Vol 29 (10) ◽  
pp. 2812-2815
Author(s):  
Yang-zhu LU ◽  
Xin-you ZHANG ◽  
Yu QI
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Spam Filtering

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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