Classification of Crop Residue Cover in High-Resolution RGB Images Using Machine Learning

HighlightsA machine learning framework estimated residue cover in RGB images taken at three resolutions from 88 locations.The best results primarily used texture features, the RFE-SVM feature selection method, and the SVM classifier.Accounting for shadows and plants plus modifying and optimizing the texture features may improve performance.An automated system developed using machine learning is a viable strategy to estimate residue cover from RGB images obtained with handheld or UAV platforms.Abstract. Maintaining plant residue on the soil surface contributes to sustainable cultivation of arable land. Applying machine learning methods to RGB images of residue could overcome the subjectivity of manual methods. The objectives of this study were to use supervised machine learning while identifying the best feature selection method, the best classifier, and the most effective image feature types for classifying residue levels in RGB imagery. Imagery was collected from 88 locations in 40 row-crop fields in five Missouri counties between early May and late June in 2018 and 2019 using a tripod-mounted camera (0.014 cm pixel-1 ground sampling distance, GSD) and an unmanned aerial vehicle (UAV, 0.05 and 0.14 GSD). At each field location, 50 contiguous 0.3 × 0.2 m region of interest (ROI) images were extracted from the imagery, resulting in a dataset of 4,400 ROI images at each GSD. Residue percentages for ground truth were estimated using a bullseye grid method (n = 100 points) based on the 0.014 GSD images. Representative color, texture, and shape features were extracted and evaluated using four feature selection methods and two classifiers. Recursive feature elimination using support vector machine (RFE-SVM) was the best feature selection method, and the SVM classifier performed best for classifying the amount of residue as a three-class problem. The best features for this application were associated with texture, with local binary pattern (LBP) features being the most prevalent for all three GSDs. Shape features were irrelevant. The three residue classes were correctly identified with 88%, 84%, and 81% 10-fold cross-validation scores for the 2018 training data and 81%, 69%, and 65% accuracy for the 2019 testing data in decreasing resolution order. Converting image-wise data (0.014 GSD) to location residue estimates using a Bayesian model showed good agreement with the location-based ground truth (r2 = 0.90). This initial assessment documents the use of RGB images to match other methods of estimating residue, with potential to replace or be used as a quality control for line-transect assessments. Keywords: Feature selection, Soil erosion, Support vector machine, Texture features, Unmanned aerial vehicle.

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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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Implementasi teknik seleksi fitur pada klasifikasi malware Android menggunakan support vector machine (SVM)

Repositor ◽

10.22219/repositor.v1i1.1 ◽

2019 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Hendra Saputra ◽

Setio Basuki ◽

Mahar Faiqurahman

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Chi Square ◽

Android Malware ◽

Correlation Based Feature Selection ◽

Selection Of

AbstrakPertumbuhan Malware Android telah meningkat secara signifikan seiring dengan majunya jaman dan meninggkatnya keragaman teknik dalam pengembangan Android. Teknik Machine Learning adalah metode yang saat ini bisa kita gunakan dalam memodelkan pola fitur statis dan dinamis dari Malware Android. Dalam tingkat keakurasian dari klasifikasi jenis Malware peneliti menghubungkan antara fitur aplikasi dengan fitur yang dibutuhkan dari setiap jenis kategori Malware. Kategori jenis Malware yang digunakan merupakan jenis Malware yang banyak beredar saat ini. Untuk mengklasifikasi jenis Malware pada penelitian ini digunakan Support Vector Machine (SVM). Jenis SVM yang akan digunakan adalah class SVM one against one menggunakan Kernel RBF. Fitur yang akan dipakai dalam klasifikasi ini adalah Permission dan Broadcast Receiver. Untuk meningkatkan akurasi dari hasil klasifikasi pada penelitian ini digunakan metode Seleksi Fitur. Seleksi Fitur yang digunakan ialah Correlation-based Feature Selection (CSF), Gain Ratio (GR) dan Chi-Square (CHI). Hasil dari Seleksi Fitur akan di evaluasi bersama dengan hasil yang tidak menggunakan Seleksi Fitur. Akurasi klasifikasi Seleksi Fitur CFS menghasilkan akurasi sebesar 90.83% , GR dan CHI sebesar 91.25% dan data yang tidak menggunakan Seleksi Fitur sebesar 91.67%. Hasil dari pengujian menunjukan bahwa Permission dan Broadcast Receiver bisa digunakan dalam mengklasifikasi jenis Malware, akan tetapi metode Seleksi Fitur yang digunakan mempunyai akurasi yang berada sedikit dibawah data yang tidak menggunakan Seleksi Fitur. Kata kunci: klasifikasi malware android, seleksi fitur, SVM dan multi class SVM one agains one Abstract Android Malware has growth significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each types of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type wiil be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used are Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). Result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicate that permission and broadcast receiver can be used in classyfing type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection. Keywords: Classification Android Malware, Feature Selection, SVM and Multi Class SVM one against one

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Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

10.21203/rs.3.rs-338866/v1 ◽

2021 ◽

Author(s):

Chunyuan Wang ◽

Yatao Zhang ◽

Xinge Jiang ◽

Feifei Liu ◽

Zhimin Zhang ◽

...

Keyword(s):

Heart Failure ◽

Congestive Heart Failure ◽

Feature Selection ◽

Time Scales ◽

Feature Selection Method ◽

Selection Method ◽

Multiple Time Scales ◽

Support Vector ◽

Svm Classifier ◽

Selection Scheme

Abstract This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use different machine learning algorithms to optimize the classification of CHF and normal sinus rhythms subject (NSR). In fact, the full utilization of traditional HRV features can also improve classification accuracy. The proposed method constructs a multi-time-scales feature matrix according to traditional HRV features that exhibit good stability in multiple time-scales and differences in different time-scales. The multi-scales features yield better performance than the traditional single-time-scales features when the features are fed into a support vector machine (SVM) classifier, and the results of the SVM classifier exhibit a sensitivity, a specificity, and an accuracy of 99.52%, 100.00%, and 99.83%, respectively. These results indicate that the proposed feature selection method can effectively reduce redundant features and computational load when used for automatic diagnosis of CHF.

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Breast Cancer Prediction using SVM with PCA Feature Selection Method

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952277 ◽

2019 ◽

pp. 969-978

Author(s):

Akshya Yadav ◽

Imlikumla Jamir ◽

Raj Rajeshwari Jain ◽

Mayank Sohani

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Learning Algorithm ◽

Feature Selection Method ◽

Selection Method ◽

Training Dataset ◽

Support Vector ◽

Improved Accuracy

Cancer has been characterized as one of the leading diseases that cause death in humans. Breast cancer, being a subtype of cancer, causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. Also, the medical tests conducted in hospitals for detecting cancer is expensive and is difficult for any common man to afford. To counter these problems, in this paper, we use the concept of applying Support Vector machine a Machine Learning algorithm to predict whether a person is prone to breast cancer. We evaluate the performance of this algorithm by calculating its accuracy and apply a min-max scaling method so as to counter and overcome the problem of overfitting and outliers. After scaling of the dataset, we apply a feature selection method called Principle component analysis to improve the algorithms accuracy by decreasing the number of parameters. The final algorithm has improved accuracy with the absence of overfitting and outliers, thus this algorithm can be used to develop and build systems that can be deployed in clinics, hospitals and medical centers for early and quick diagnosis of breast cancer. The training dataset is from the University of Wisconsin (UCI) Machine Learning Repository which is used to evaluate the performance of the Support vector machine by calculating its accuracy.

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Sentiment Analysis Using Hybrid Feature Selection Techniques

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v4n1y2020.pp29-40 ◽

2020 ◽

Vol 4 (1) ◽

pp. 29

Author(s):

Sasan Sarbast Abdulkhaliq ◽

Aso Mohammad Darwesh

Keyword(s):

Machine Learning ◽

Social Media ◽

Feature Selection ◽

Classification Accuracy ◽

Feature Selection Method ◽

Selection Method ◽

Machine Learning Algorithms ◽

Feature Subset Selection ◽

Support Vector ◽

Feature Subset

Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.

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Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India

Frontiers in Earth Science ◽

10.3389/feart.2021.701837 ◽

2021 ◽

Vol 9 ◽

Author(s):

Naresh Mali ◽

Varun Dutt ◽

K. V. Uday

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Slope Failure ◽

Feature Selection Method ◽

Failure Prediction ◽

Selection Method ◽

Support Vector ◽

Selection Methods ◽

Slope Failures ◽

Causal Factors

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

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An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection

Mathematics ◽

10.3390/math9111226 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1226

Author(s):

Saeed Najafi-Zangeneh ◽

Naser Shams-Gharneh ◽

Ali Arjomandi-Nezhad ◽

Sarfaraz Hashemkhani Zolfani

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Standard Deviation ◽

Analytical Formula ◽

Feature Selection Method ◽

Selection Method ◽

Performance Measure ◽

Learning Approaches ◽

Training Costs ◽

Professional Employees

Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot be described by a scientific-analytical formula. Therefore, machine learning approaches are the best tools for this aim. This paper presents a three-stage (pre-processing, processing, post-processing) framework for attrition prediction. An IBM HR dataset is chosen as the case study. Since there are several features in the dataset, the “max-out” feature selection method is proposed for dimension reduction in the pre-processing stage. This method is implemented for the IBM HR dataset. The coefficient of each feature in the logistic regression model shows the importance of the feature in attrition prediction. The results show improvement in the F1-score performance measure due to the “max-out” feature selection method. Finally, the validity of parameters is checked by training the model for multiple bootstrap datasets. Then, the average and standard deviation of parameters are analyzed to check the confidence value of the model’s parameters and their stability. The small standard deviation of parameters indicates that the model is stable and is more likely to generalize well.

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NIMG-46. RADIOGENOMIC FEATURES PREDICT CLINICALLY RELEVANT GENOME-WIDE ALTERATION SIGNATURES IN GLIOBLASTOMA

Neuro-Oncology ◽

10.1093/neuonc/noaa215.659 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii158-ii158

Author(s):

Nicholas Nuechterlein ◽

Beibin Li ◽

James Fink ◽

David Haynor ◽

Eric Holland ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Molecular Subtypes ◽

Feature Selection Method ◽

Selection Method ◽

Versus Group ◽

Mri Features ◽

Genome Wide ◽

Group 2 ◽

Group 1

Abstract BACKGROUND Previously, we have shown that combined whole-exome sequencing (WES) and genome-wide somatic copy number alteration (SCNA) information can separate IDH1/2-wildtype glioblastoma into two prognostic molecular subtypes (Group 1 and Group 2) and that these subtypes cannot be distinguished by epigenetic or clinical features. However, the potential for radiographic features to discriminate between these molecular subtypes has not been established. METHODS Radiogenomic features (n=35,400) were extracted from 46 multiparametric, pre-operative magnetic resonance imaging (MRI) of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive, all of whom have corresponding WES and SCNA data in The Cancer Genome Atlas. We developed a novel feature selection method that leverages the structure of extracted radiogenomic MRI features to mitigate the dimensionality challenge posed by the disparity between the number of features and patients in our cohort. Seven traditional machine learning classifiers were trained to distinguish Group 1 versus Group 2 using our feature selection method. Our feature selection was compared to lasso feature selection, recursive feature elimination, and variance thresholding. RESULTS We are able to classify Group 1 versus Group 2 glioblastomas with a cross-validated area under the curve (AUC) score of 0.82 using ridge logistic regression and our proposed feature selection method, which reduces the size of our feature set from 35,400 to 288. An interrogation of the selected features suggests that features describing contours in the T2 abnormality region on the FLAIR MRI modality may best distinguish these two groups from one another. CONCLUSIONS We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups. This algorithm may be applied to future prospective studies to assess the utility of MRI as a surrogate for costly prognostic genomic studies.

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Radiogenomic modeling predicts survival-associated prognostic groups in glioblastoma

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab004 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Nicholas Nuechterlein ◽

Beibin Li ◽

Abdullah Feroze ◽

Eric C Holland ◽

Linda Shapiro ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Molecular Subtypes ◽

Feature Selection Method ◽

Area Under The Curve ◽

Selection Method ◽

Recursive Feature Elimination ◽

Signal Abnormality ◽

Mri Features ◽

Mri Scans

Abstract Background Combined whole-exome sequencing (WES) and somatic copy number alteration (SCNA) information can separate isocitrate dehydrogenase (IDH)1/2-wildtype glioblastoma into two prognostic molecular subtypes, which cannot be distinguished by epigenetic or clinical features. The potential for radiographic features to discriminate between these molecular subtypes has yet to be established. Methods Radiologic features (n = 35 340) were extracted from 46 multisequence, pre-operative magnetic resonance imaging (MRI) scans of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive (TCIA), all of whom have corresponding WES/SCNA data. We developed a novel feature selection method that leverages the structure of extracted MRI features to mitigate the dimensionality challenge posed by the disparity between a large number of features and the limited patients in our cohort. Six traditional machine learning classifiers were trained to distinguish molecular subtypes using our feature selection method, which was compared to least absolute shrinkage and selection operator (LASSO) feature selection, recursive feature elimination, and variance thresholding. Results We were able to classify glioblastomas into two prognostic subgroups with a cross-validated area under the curve score of 0.80 (±0.03) using ridge logistic regression on the 15-dimensional principle component analysis (PCA) embedding of the features selected by our novel feature selection method. An interrogation of the selected features suggested that features describing contours in the T2 signal abnormality region on the T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI sequence may best distinguish these two groups from one another. Conclusions We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups.

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