Feature Selection and Performance Comparison of Various Machine Learning Classifiers for Analyzing Students’ Performance Using Rapid Miner

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

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Hybrid Machine Learning Classifiers for Indoor User Localization Problem

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8375.0110321 ◽

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/

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

2019 IEEE/ACM Symposium on Software Engineering in Africa (SEiA) ◽

10.1109/seia.2019.00009 ◽

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

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Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Future Internet ◽

10.3390/fi14010016 ◽

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%.

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