Combination Of Feature Selection Techniques Research Articles

Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information

BackgroundAtrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation. MethodsIn total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques. ResultsThe proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm. ConclusionOur results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.

An Ensemble Framework Approach to Crop Type Prediction Using Feature Selection and Multiclass Classification

Crop type classification plays a crucial role in modern agriculture, aiding in yield prediction, resource management, and land-use planning. This paper presents a comprehensive framework for crop type classification utilizing a combination of feature selection techniques, robust classification Algorithm, and a Support Vector Machine (SVM)-based multiclass classification approach. The proposed framework begins with a novel feature selection process that identifies the most relevant attributes from the Agricultural Data and Rainfall data. This feature selection step is essential for reducing data dimensionality, enhancing classification accuracy, and improving model interpretability. Following feature selection, a state-of-the-art multiclass classification strategy based on Support Vector Machines is employed. SVMs are known for their capability to handle high-dimensional data and have demonstrated superior performance in various classification tasks. In this framework, SVMs are adapted to handle multiclass crop type classification efficiently. The model is trained on the selected features and optimized using hyperparameter tuning techniques to ensure robust performance.

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.

Komparasi Performa Tree-Based Classifier Untuk Deteksi Anomali Pada Data Berdimensi Tinggi dan Tidak Seimbang

Anomaly detection is one solution to overcome the issue of data network traffic security, but is faced with the challenge of high data dimensionality and imbalanced data. High-dimensional and imbalanced data can affect the performance of the detection system. Therefore we need a feature selection technique that can reduce the dimensionality of the data by eliminating irrelevant features. In addition, the selected features need to be validated with the right classification algorithm to produce high anomaly detection performance. The purpose of this study is to produce a combination of feature selection techniques and appropriate classification algorithms to produce a system that is able to detect attacks on high-dimensional and imbalanced data. Chi-square feature selection technique was used to eliminate irrelevant features. To determine the ideal classification algorithm, in this study, a comparison of the performance of the tree-based classifer algorithm was carried out. This study also examines the performance of classification techniques in detecting traffic on high-dimensional and unbalanced data. Several Tree-based classification algorithms such as REPTree, J48, Random Tree and Random Forest were tested and compared. Testing with the best performance as a recommendation for the ideal combination of feature selection techniques and classification algorithms. This research produces an anomaly detection system that has high performance. For experimental data, the CICIDS-2017 dataset is used, which has high data dimensionality and contains unbalanced data. The test results show that Random Tree has an accuracy of 99.983% and Random Forest 99.984%.

A review of dimensionality reduction methods applied on clinical data of diabetic neuropathy complaints

Aim of this paper is to apply Dimensionality Reduction techniques on a large clinical data of Type II diabetes patients in identifying the causes and symptoms that they tend to develop neuropathic complications. Data preprocessing is an essential technique using Machine Learning (ML) and Data Mining are ineffective for big data analytics. An effective approach to dimensionality reduction is reduction of the number of independent/dependent variables which are necessary for our analysis. These processes help in identifying the features to be considered for selection and extraction avoiding the redundant and irrelevant features by choosing subset features which are a linear combination of the original. Both supervised and unsupervised learning are applied for prediction and further analysis. This paper primarily focuses on Supervised Learning, the variables are known beforehand. Combination of feature selection techniques and ML algorithms are used to support practitioners with the best methods for feature reduction and extraction.

Optimised feature selection for early cancer detection

Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) status report for the year of 2020, suggests the occurrence of 10.0 million cancer deaths and 19.3 million new cancer cases. Clearly, cancer incidence and mortality are rapidly growing worldwide. Also, the leading causes of cancer deaths are found to be lung cancer and breast cancer. Cancer cells are having the probability of spreading to other parts of the body too. Most chronic cancers are not curable, but some can be controlled for a few months or years. Also, there is a possibility of high rate of relapse of the disease. These remissions can be partial or complete. But, if detected early, certain cancers can be treated by surgery, chemotherapy, and radiation therapy. This research work focuses on detecting cancer in its early stage so that right measures can be taken to combat the disease. In this attempt to create a beneficial working model, the combination of Artificial Neural Network (ANN), Convolution Neural Network, Graph based Neural Network with Genetic Algorithm (GA) have proven to be successful. As a proof of concept, we present a combination of feature selection techniques that can effectively reduce the feature set and optimize the classification techniques. The proposed method, when applied on a benchmark dataset, gave a higher accuracy by selecting most relevant 7 features out of 10 with an accuracy of 95.7%. Using Convolution Neural Network, the accuracy improved to 98.3% with optimal hyperparameter tuning.

A Novel Hybrid Classification Method Based on the Opposition-Based Seagull Optimization Algorithm

In practice, classification problems have appeared in many scientific fields, including finance, medicine and industry. It is critically important to develop an effective and accurate classification model. Although numerous useful classifiers have been proposed, they are unstable, sensitive to noise and slow in computation. To overcome these drawbacks, the combination of feature selection techniques with traditional machine learning models is of great help. In this paper, a novel feature selection method called the opposition-based seagull optimization algorithm (OSOA) is proposed and studied. The OSOA is constructed based on an SOA whose population is determined by the opposition-based learning (OBL) algorithm. To evaluate its overall classification performance, some measures, including classification accuracy, number of selected features, receiver operating characteristic curve (ROC), and computation time, are adopted. The empirical results indicate that the suggested method exhibits higher or similar accuracy and computational efficiency in comparison with genetic algorithm (GA)-, simulated annealing (SA)-, and Fisher score (FS)-based classification models. The experimental results show that the OSOA is a computationally efficient feature selection technique that has the ability to select relevant variables. Furthermore, it performs well with high-dimensional data whose number of variables exceeds the number of samples. Thus, the OSOA is an effective approach for the enhancement of classification performance.

A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies.

Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in the computational model such as ZC3H11A, VAX2, MAF1, and ZFP91 are related to breast cancer and other types of cancer.