A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis

Deng, Shuiguang; Xu, Yifei; Li, Li; Liu, Yuanyuan; He, Yong

doi:10.1016/j.jfoodeng.2013.05.024

Cited by 40 publications

(14 citation statements)

References 24 publications

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“…Pixel based classification on super-pixel over-segmentation, clustering of dense SIFTS features into visual words and bagof-visual-word super-pixel classification using SVMs (support vector machine) etc methods have used. Shuiguang Deng et al [39] have proposed a feature-selection algorithm based on support vector machinemulticlass for hyper spectral analysis. This article has proposed a novel feature selection algorithm named support vector machine-multiclass forward feature selection (SVM-MFFS).…”

Section: Classification Technique F Support Vector Machinementioning

confidence: 99%

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

Sethy¹,

Panda²,

Behera³

et al. 2017

IJET

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Abstract-This paper reports involvement of image processing and machine vision technique to detect and count of fruits on-tree, in field condition, have been reviewed. In addition, this paper also associated with the grading of fruits in post-harvesting. Different types of algorithms are available for counting and to extract the feature of fruit characters by capturing the on-tree fruit image by any conventional RGB camera. With the help of this counting algorithm and feature extraction technique, fruit is detected and counted. This work also surveys grading method applied to the post-harvest fruits. Grading method involves: identification of mature & immature fruits, intact & diseased fruits and also predict the weight of the fruit from its shape. The grading of fruit can be done by using different types of the classifier. The main features, drawback and future prospective of previous work in this area are summarized.

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Section: Classification Technique F Support Vector Machinementioning

confidence: 99%

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

Sethy¹,

Panda²,

Behera³

et al. 2017

IJET

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“…The first part of the objective function tries to maximize the margin between both classes in the feature space, whereas the second part minimizes the misclassification error. The positive real constant C should be considered as a tuning parameter in the algorithm [16].…”

Section: Fig 2: Svm Classificationmentioning

confidence: 99%

Formulation of Feature Selection with Support Vector Machine

Prajapati¹,

Patle²

2015

IJCA

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Basic question arises when classification came in picture classification accuracy, ensemble size, and computational complexity. Feature selection is importance for improvement and performance of classification algorithm. Classification algorithm may not scale up to the size of the full feature set either in sample or time but with feature selection help us to better understand the domain with Cheaper to collect a subset of predictors and Safer to collect a reduced subset of predictors. An important pre-processing step in classification tasks is called as, Feature selection its aims to minimize both the classification error rate and the number of features for inference knowledge in any domain. Feature selection is Minimum set F that achieves maximum classification performance of T (for a given set of classifiers and classification performance metrics). This paper proposes feature selection methodology which includes ranking, information gain and filter method concept. After the feature subset train SVM with RBF kernel for classification.

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“…We note, however, that few studies have considered the direct application of SVM-RFE to the problem of NIR samples classification. As mentioned in [23], SVM-RFE can be too computationally expensive, specially when only one least useful feature is removed at each iteration step. Also, SVM-RFE may be unstable with respect to variations in the training data [24].…”

Section: Introductionmentioning

confidence: 99%

A spectral envelope approach towards effective SVM-RFE on infrared data

Spetale

Bulacio

Guillaume³

et al. 2016

Pattern Recognition Letters

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Infrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the midinfrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. To effectively handle this type of data, suitable dimensionality reduction methods are required. In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented. The method exploits the information content at peaks of the spectral envelope functions which characterize NIR/MIR spectra datasets. Experimental evaluation across different NIR/MIR application domains shows that the proposed method is useful for the induction of compact and accurate SVM classifiers for qualitative NIR/MIR applications involving stringent interpretability or time processing requirements.

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A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis

Cited by 40 publications

References 24 publications

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

Formulation of Feature Selection with Support Vector Machine

A spectral envelope approach towards effective SVM-RFE on infrared data

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