An Investigation of Spectral Band Selection for Hyperspectral LiDAR Technique

Hyperspectral LiDAR (HSL) has been widely discussed in recent years, which attracts increasing attention of the researchers in the field of electronic information technology. With the application of supercontinuum laser source, it is now possible to develop an HSL system, which can collect spectral and spatial information of targets simultaneously. Meanwhile, eye-safety and miniature HSL device with multiple spectral bands are given more priorities in on-site applications. In this paper, we tempt to investigate how to select spectral bands with a selection method. The proposed method consists of three steps: first, the variances among the classes based on hyperspectral feature parameters, termed inter-class variances, are calculated; second, the channels are sorted based on corresponding variances in descending order, and those with the two highest values are adopted as the initial input of classification; finally, the channels are selected successively from the rest of the sorted sequence until the classification accuracy reaches 100%. To test the performance of the proposed method, we collect 91/71-channel hyperspectral measurements of four different categories of materials with 5 nm spectral resolution using an acousto-optic tunable filter (AOTF) based HSL. Experimental results demonstrate that the proposed method could achieve higher classification accuracy than a random band selection method with different classifiers (naïve Bayes (NB) and support vector machine (SVM)) regardless of classification feature parameters (echo maximum and reflectance). To reach 100% accuracy, it demands 8–9 channels on average by echo maximum and 4–5 channels on average by reflectance based on NB classifier; these figures are 3–4 by echo maximum and 2–3 by reflectance with SVM classifier. The proposed method can complete classification task much faster than the random selection method. We further confirm the specific channels for the classification of different materials, and find that the optimal channels vary with different materials. The experimental results prove that the optimal band selection of HSL system for classification is reliable.

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A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-190134 ◽

2021 ◽

Vol 24 (4) ◽

pp. 289-301

Author(s):

B. Venkatesh ◽

J. Anuradha

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Classification Accuracy ◽

Performance Metrics ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Svm Classifier ◽

Binary Particle Swarm Optimization ◽

Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

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Feature Selection Method Based on Mutual Information and Support Vector Machine

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142150021x ◽

2021 ◽

pp. 2150021

Author(s):

Gang Liu ◽

Chunlei Yang ◽

Sen Liu ◽

Chunbao Xiao ◽

Bin Song

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Mutual Information ◽

Classification Accuracy ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Svm Classifier ◽

Standard Data ◽

Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

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Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

Journal of Imaging ◽

10.3390/jimaging6090087 ◽

2020 ◽

Vol 6 (9) ◽

pp. 87 ◽

Cited By ~ 1

Author(s):

Ruben Moya Torres ◽

Peter W.T. Yuen ◽

Changfeng Yuan ◽

Johathan Piper ◽

Chris McCullough ◽

...

Keyword(s):

Mutual Information ◽

Classification Accuracy ◽

High Efficiency ◽

Spectral Band ◽

Band Selection ◽

Practical Applications ◽

Remote Sensing Classification ◽

Spectral Bands ◽

Kennedy Space ◽

First Time

Despite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification. This apparently disagrees with the theoretical model of the ‘curse of dimensionality’ phenomenon, without apparent explanations. If it were true, then BS would be deemed as an academic piece of research without real benefits to practical applications. This paper presents a spatial spectral mutual information (SSMI) BS scheme that utilizes a spatial feature extraction technique as a preprocessing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the efficiency of the BS. Through the SSMI BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic that peaks at about 20 bands has been observed for the very first time. The performance of the proposed SSMI BS scheme has been validated through 6 hyperspectral imaging (HSI) datasets (Indian Pines, Botswana, Barrax, Pavia University, Salinas, and Kennedy Space Center (KSC)), and its classification accuracy is shown to be approximately 10% better than seven state-of-the-art BS schemes (Saliency, HyperBS, SLN, OCF, FDPC, ISSC, and Convolution Neural Network (CNN)). The present result confirms that the high efficiency of the BS scheme is essentially important to observe and validate the Hughes’ phenomenon in the analysis of HSI data. Experiments also show that the classification accuracy can be affected by as much as approximately 10% when a single ‘crucial’ band is included or missed out for classification.

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Evaluation of the Performance for Popular Three Classifiers on Spam Email without using FS methods

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2021.16.9 ◽

2021 ◽

Vol 16 ◽

pp. 121-132

Author(s):

Ghada AL-Rawashdeh ◽

Rabiei Bin Mamat ◽

Jawad Hammad Rawashdeh

Keyword(s):

Classification Accuracy ◽

Nearest Neighbor ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Svm Classifier ◽

K Nearest Neighbor ◽

High Increase ◽

Simulation Results ◽

Using Data

Email is one of the most economical and fast communication means in recent years; however, there has been a high increase in the rate of spam emails in recent times due to the increased number of email users. Emails are mainly classified into spam and non-spam categories using data mining classification techniques. This paper provides a description and comparative for the evaluation of effective classifiers using three algorithms - namely k-nearest neighbor, Naive Bayesian, and support vector machine. Seven spam email datasets were used to conducted experiment in the MATLAB environment without using any feature selection method. The simulation results showed SVM classifier to achieve a better classification accuracy compared to the K-NN and NB.

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Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

PLoS ONE ◽

10.1371/journal.pone.0253385 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253385

Author(s):

Yuanyuan Shi ◽

Junyu Zhao ◽

Xianchong Song ◽

Zuoyu Qin ◽

Lichao Wu ◽

...

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Prediction Model ◽

Goodness Of Fit ◽

Spectral Band ◽

Organic Matter Content ◽

Band Selection ◽

Support Vector ◽

Spectral Bands ◽

Modeling Accuracy

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

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Classification of SSVEP-based BCIs using Genetic Algorithm

Journal Of Big Data ◽

10.1186/s40537-021-00478-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hamideh Soltani ◽

Zahra Einalou ◽

Mehrdad Dadgostar ◽

Keivan Maghooli

Keyword(s):

Genetic Algorithm ◽

Dimension Reduction ◽

Classification Accuracy ◽

Bayesian Method ◽

Computer Interface ◽

Support Vector ◽

Svm Classifier ◽

Effective Dimension ◽

Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

BioMed Research International ◽

10.1155/2016/4783801 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zhu-Hong You ◽

Yu-Hong Fang ◽

Yu-Jun Zhao ◽

...

Keyword(s):

Protein Sequences ◽

Relevance Vector Machine ◽

Experimental Results ◽

Computational Method ◽

Support Vector ◽

Svm Classifier ◽

Local Phase ◽

Local Phase Quantization ◽

Phase Quantization ◽

Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

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A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v11i6.4846 ◽

2015 ◽

Vol 11 (6) ◽

pp. 4 ◽

Cited By ~ 2

Author(s):

Xianfeng Yuan ◽

Mumin Song ◽

Fengyu Zhou ◽

Yugang Wang ◽

Zhumin Chen

Keyword(s):

Fault Diagnosis ◽

Classification Accuracy ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Service Robot ◽

Support Vector ◽

Svm Classifier ◽

Processing Unit ◽

Training Method ◽

Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

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Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

10.21203/rs.3.rs-273617/v1 ◽

2021 ◽

Author(s):

Rejith K.N ◽

Kamalraj Subramaniam ◽

Ayyem Pillai Vasudevan Pillai ◽

Roshini T V ◽

Renjith V. Ravi ◽

...

Keyword(s):

Frequency Domain ◽

Classification Accuracy ◽

Machine Learning Algorithms ◽

Signal Frequency ◽

Spectral Energy ◽

Support Vector ◽

Svm Classifier ◽

Time Frequency ◽

Energy Entropy ◽

Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

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