scholarly journals Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Anam Mustaqeem ◽  
Syed Muhammad Anwar ◽  
Muahammad Majid
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Multiclass Classification ◽  
Data Repository ◽  
Support Vector ◽  
Kappa Statistics ◽  
Health Issues ◽  
Feature Selection Technique ◽  
Life Threatening ◽  
The University

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

Developing Machine Learning Models to Predict Roadway Traffic Noise: An Opportunity to Escape Conventional Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 158-172 ◽  
Author(s):  
Mohamad Ali Khalil ◽  
Khaled Hamad ◽  
Abdallah Shanableh
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
United Arab Emirates ◽  
Traffic Noise ◽  
Free Field ◽  
Model Performance ◽  
Noise Model ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Performance Results

Accurate prediction of roadway traffic noise remains challenging. Many researchers continue to improve the performance of their models by either adding more variables or improving their modeling algorithms. In this research, machine learning (ML) modeling techniques were developed to predict roadway traffic noise accurately. The ML techniques applied were: regression decision trees, support vector machine, ensembles, and artificial neural network. The parameters of each of these models were fine-tuned to achieve the best performance results. In addition, a state-of-the-art hybrid feature-selection technique has been employed to select a minimum set of input features (variables) while maintaining the accuracy of the developed models. By optimizing the number of features used in the model, the resources needed to develop and utilize a model to predict roadway noise would be less, hence decreasing the development cost. The proposed approach has been applied to develop a free-field roadway traffic noise model for Sharjah City in the United Arab Emirates. The best developed ML model was compared with a conventional regression model which was developed earlier under the same conditions. The cross-validated results clearly indicate that the best ML model outperformed the regression modeling. The performance of the ML model was also assessed after reducing the number of its input features based on the outcome of the feature-selection algorithm; the model performance was slightly affected. This result emphasizes the importance of considering only features that greatly influence the roadway traffic noise.

scholarly journals Ensemble Machine Learning Approaches for Proteogenomic Cancer Studies

2020 ◽  
Author(s):  
Yulan Liang ◽  
Amin Gharipour ◽  
Erik Kelemen ◽  
Arpad Kelemen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Performance Criteria ◽  
Stable Set ◽  
Support Vector ◽  
Kappa Statistics ◽  
Learning Approaches ◽  
Ensemble Machine Learning ◽  
Homogeneous Ensemble

Abstract Background: The identification of important proteins is critical for medical diagnosis and prognosis in common diseases. Diverse sets of computational tools were developed for omics data reductions and protein selections. However, standard statistical models with single feature selection involve the multi-testing burden of low power with the available limited samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models, and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics data sets: 1) binary putative homologous recombination deficiency positive or negative; and 2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown). We conducted and compared various machine learning approaches with homogeneous ensemble feature selection including random forest, support vector machine, and neural network for predicting both binary and multiple class outcomes. Various performance criteria including sensitivity, specificity, kappa statistics were used to assess the prediction consistency and accuracy. Results: With the proposed three-stage homogeneous ensemble feature selection approaches, prediction accuracy can be improved with the limited sample through continuously reducing errors and redundancy, i.e. Treebag provided 83% prediction accuracy (85% sensitivity and 81% specificity) for binary ovarian outcomes. For mRNA multi-classes classification, our approach provided even better accuracy with increased sample size. Conclusions: Despite the different prediction accuracies from various models, homogeneous ensemble feature selection proposed identified consistent sets of top ranked important markers out of 9606 proteins linked to the binary disease and multiple mRNA class outcomes.

scholarly journals Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

2020 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Classification Performance ◽  
Support Vector ◽  
Product Reviews ◽  
Feature Selection Technique ◽  
Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

scholarly journals Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019 ◽  
Vol 8 (9S4) ◽  
pp. 39-47
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Cuckoo Search ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Performance Factors

Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

scholarly journals A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 699
Author(s):  
Yogendra Singh Solanki ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
Zbigniew Leonowicz ◽  
Vadim Bolshev ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Decision Tree ◽  
Search Algorithm ◽  
Cancer Prognosis ◽  
Support Vector ◽  
Kappa Statistics ◽  
Genetic Search ◽  
Tree Classifier

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.

scholarly journals Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

2021 ◽  
Vol 4 (1) ◽  
pp. 22-27
Author(s):  
Saikin Saikin ◽  
◽  
Sofiansyah Fadli ◽  
Maulana Ashari ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Training Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Validation Data ◽  
Machine Method ◽  
Testing Data ◽  
The Impact

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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