Feature Selection, Online Feature Selection Techniques for Big Data Classification: - A Review

Chang

J Ambient Intell Human Comput

et al. 2021

The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define “content features” and build lexicons to determine the attributes’ values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine–recursive feature elimination (SVM–RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

Section: Feature Selectionmentioning

confidence: 99%

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach

Chang

J Ambient Intell Human Comput

et al. 2021

2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

“…Feature selection is a method of selecting the features in your data that contribute the most to the prediction variable or output [13]. There are advantages to performing feature selection before modeling your data such as reduce data redundancy which means there is less chance of making decisions based on noise and this will reduce overfitting, and help the algorithms to train faster and thus reducing the time and complexity.…”

Section: Features Selectionmentioning

confidence: 99%

“…Covid-19 was declared a global pandemic by the World Health Organization (WHO) and was reported as an international concern for public health. After infection, symptoms begin to appear in a period ranging from (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) days. The popular symptoms of Covid-19 are dry cough, tiredness, and fever [1].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Covid-19 Data Using Various Algorithms

Krajah

Almadani

Saadeh

et al. 2021

The world strives to combat Covid-19, which has spread very quickly across the world after the disease was first confirmed in Dec 2019 in Wuhan District, China. This disease has infected millions and kills thousands in most countries until today. Machine learning and artificial intelligence are promising technologies that have an effective role in developing business in many sectors. Recent studies have shown the importance of using artificial intelligence and machine learning in the health sector. The results show that they lead to increased processing capability, reliability, and even superiority over the human's performance in particular healthcare tasks. In this research, we applied several machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and k-Nearest Neighbors (KNN) on Covid-19 dataset provided via Kaggle website to predict the patient's death or survival depending on the patient's health status and some other factors. To evaluate the performance of these algorithms, we used the confusion matrix.

“…The exponential growth of data from different sources has increased the number of features and attributes available for analytics. Consequently, selecting the appropriate features for the job is clearly necessary to reducing the dimensionality of data and improving the performance of machinelearning algorithms (Devi and Sabrigiriraj 2018;Shah and Patel 2016;Visalakshi and Radha 2014). The feature selection (FS) goal is to eliminate irrelevant and redundant data to improve prediction accuracy and reduce execution time (Visalakshi and Radha 2014).…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

Alothali

Hayawi

Alashwal

2021

Soc. Netw. Anal. Min.

The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).