Hybrid Model of Correlation Based Filter Feature Selection and Machine Learning Classifiers Applied on Smart Meter Data Set

2019 ◽  
Author(s):  
Janvier Omar Sinayobye ◽  
Kyanda Swaib Kaawaase ◽  
Fred N. Kiwanuka ◽  
Richard Musabe
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Hybrid Model ◽  
Smart Meter ◽  
Data Set ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Hybrid Machine Learning Classifiers for Indoor User Localization Problem

2021 ◽  
Vol 10 (3) ◽  
pp. 49-53
Author(s):  
Hamza Turabieh ◽  
Ahmad S. Alghamdi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
User Location

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/

scholarly journals Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

2021 ◽  
Vol 50 (8) ◽  
pp. 2479-2497
Author(s):  
Buvana M. ◽  
Muthumayil K.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Nasal Congestion ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set ◽  
Physiological Measurement ◽  
Machine Learning Classifiers ◽  
Balanced Diet ◽  
Learning Classifiers

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

scholarly journals Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

10.2196/17478 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e17478 ◽  
Author(s):  
Shyam Visweswaran ◽  
Jason B Colditz ◽  
Patrick O’Halloran ◽  
Na-Rae Han ◽  
Sanya B Taneja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Surveillance System ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Data Set ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

scholarly journals Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Chandrashekar Jatoth ◽  
Rishabh Jain ◽  
Ugo Fiore ◽  
Subrahmanyam Chatharasupalli
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Blockchain Technology ◽  
Widespread Adoption ◽  
Learning Classifiers ◽  
Correlation Based Feature Selection

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.

scholarly journals Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

2021 ◽  
Vol 12 (6) ◽  
pp. 2650-2665
Author(s):  
Ritu Aggrawal, Saurabh Pal
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Algorithms ◽  
Model Verification ◽  
P Value ◽  
Accuracy Score ◽  
Data Set ◽  
Machine Learning Classifiers ◽  
Backward Elimination ◽  
Learning Classifiers

Background: Early speculation of cardiovascular disease can help determine the lifestyle change options of high-risk patients, thereby reducing difficulties. We propose a coronary heart disease data set analysis technique to predict people’s risk of danger based on people’s clinically determined history. The methods introduced may be integrated into multiple uses, such for developing decision support system, developing a risk management network, and help for experts and clinical staff. Methods: We employed the Framingham Heart study dataset, which is publicly available Kaggle, to train several machine learning classifiers such as logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), random forest (RF) and gradient boosting classifier (GBC) for disease prediction. The p-value method has been used for feature elimination, and the selected features have been incorporated for further prediction. Various thresholds are used with different classifiers to make predictions. In order to estimating the precision of the classifiers, ROC curve, confusion matrix and AUC value are considered for model verification. The performance of the six classifiers is used for comparison to predict chronic heart disease (CHD). Results: After applying the p-value backward elimination statistical method on the 10-year CHD data set, 6 significant features were selected from 14 features with p <0.5. In the performance of machine learning classifiers, GBC has the highest accuracy score, which is 87.61%. Conclusions: Statistical methods, such as the combination of p-value backward elimination method and machine learning classifiers, thereby improving the accuracy of the classifier and shortening the running time of the machine.

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