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Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection
2021
International Journal of Interactive Mobile Technologies
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Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for ...
A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve ...
Evidently, from the results of the experiments, it was observed that removing redundant and irrelevant features from spam datasets using hybridized feature selection and feature extraction method in conjunction ...
doi:10.3991/ijim.v15i17.19915
fatcat:qiib3t2ycraudgg4nxeaivi3ya
A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks
2020
IEEE Access
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In this work, a deep convolutional neural network (CNN) ensemble framework for efficient DDoS attack detection in SDNs is proposed. ...
Distributed denial of service (DDoS) attacks are, perhaps, the most prevalent and exponentially-growing attack, targeting the varied and emerging computational network infrastructures across the globe. ...
Their system works by the extraction of features of interest at certain intervals in order to convert the system in lightweight mode. ...
doi:10.1109/access.2020.2976908
fatcat:xeo7o4ny4vaqddycqb26six7oy
Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
2020
Algorithms
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Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. ...
Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition. ...
However, we provided evidence through intensive experimentation that random decision forest ensemble learning of the proposed hybrid acoustic features was highly effective for speech emotion recognition ...
doi:10.3390/a13030070
fatcat:rdsq4oiar5bqtk6ezuwj277spy
Ensemble approach for developing a smart heart disease prediction system using classification algorithms
2018
Research Reports in Clinical Cardiology
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Extracting patterns that tie predictor's variables in a health science database is the topic of data mining. Existing data mining techniques are appropriate to model complex, dynamic processes. ...
Experimental results demonstrated that the ensemble model is a superior approach in terms of high predictive accuracy and reliability of diagnostics performance. ...
Acknowledgments The authors thank Allah the most beneficent and most merciful.
Disclosure The authors report no conflicts of interest in this work. ...
doi:10.2147/rrcc.s172035
fatcat:nzu6pqa5svdmfi34f4pllbfpeu
Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier
2020
Procedia Computer Science
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Experimental results have revealed that the Bagging ensemble classifier produced satisfactory results, and Bagging with Random Forest achieved better results with an accuracy of 99.02%. ...
Experimental results have revealed that the Bagging ensemble classifier produced satisfactory results, and Bagging with Random Forest achieved better results with an accuracy of 99.02%. ...
The experimental results of this study reveal that Bagging ensemble with Random Forest can be utilized to classify the normal and pathological cases of the CTG data. ...
doi:10.1016/j.procs.2020.02.248
fatcat:ty5ic7dkuveghpavmew34wtzz4
A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem
2020
Journal of Computer Science
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Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid, and ensemble method design to ...
understand the current status of improving classification output in machine learning techniques to fix problems with class imbalances. ...
Acknowledgment We would like to thank Google and UCI Machine for providing the dataset and necessary information for this this research. ...
doi:10.3844/jcssp.2020.1546.1557
fatcat:ecgaztln6fecjnege3ne3yeqoe
Exploration on Feature Extraction Schemes and Classifiers for Shaft Testing System
2010
Journal of Computers
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Applications of machine learning demand exploration Section II is concerned with the extraction of
of feature extraction methods and classifier types in order informative features ...
Although several pattern analysis and machine analysis between two feature extraction schemes (FFT
learning techniques have been used with success in and DWT) is expanded ...
doi:10.4304/jcp.5.5.679-686
fatcat:bo5xvxexgzc55dwyhjpiykkjeu
RHEM: A Robust Hybrid Ensemble Model for Students' Performance Assessment on Cloud Computing Course
2020
International Journal of Advanced Computer Science and Applications
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, and Rotation Forestwhich produced 16 new hybrid ensemble classifier models. ...
We hybridised four renowned single algorithms -Naïve Bayes, Multilayer Perceptron, k-Nearest Neighbours, and Decision Table - with four well-established ensemble algorithms -Bagging, RandomSubSpace, MultiClassClassifier ...
INTRODUCTION Innumerable data are generated and gathered in numerous fields. The big data created need to be collected, organized, and analysed in order to extract useful information. ...
doi:10.14569/ijacsa.2020.0111150
fatcat:yq2lrf6f3vb5rh7vrtpgfccfga
A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels
2021
Bulletin of Electrical Engineering and Informatics
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Scientists have suggested many addressed learning for the study of biological evidence. ...
An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification ...
[20] focused on a genetic algorithm-based hybrid system by implementing a groundbreaking hybrid feature selection algorithm using a filter-wrapper-based feature selection method to identify problems ...
doi:10.11591/eei.v10i2.2769
fatcat:tl5icunxxzha3bwcblv7jfwnla
Software Defect Prediction using Ensemble Learning: A Systematic Literature Review
2021
IEEE Access
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In [74] , researchers proposed a model based on feature selection, feature extraction, class balancing and ensemble learning. ...
In [64] , an RF (ensemble of trees that vote for the class) was combined with feature selection and data sampling. ...
doi:10.1109/access.2021.3095559
fatcat:72divlxlbjdirpmpotdeyxndi4
A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification
2021
Financial Innovation
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AbstractTo solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier ...
learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark ...
LY participated in the framework design, data preprocessing, software coding, and drafted the manuscript. KY carried out the data collection, literature investigation and edited the manuscript. ...
doi:10.1186/s40854-021-00249-x
fatcat:jaisqt6avffathkg24fifunm64
Expert System for the Identification of Review Papers Using Ensemble Learning
2021
Pakistan Social Sciences Review
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As a contribution in that direction; we develop a hybrid ensemble algorithm, called Balanced MultiBoost (BMB). ...
Creating an effective classifier in the presence of imbalanced data is a challenging task. ...
The key benefit of this class of algorithms is increased diversity in ensembles (Wang et al.,2009 ). • Hybrid ensembles combine both bagging and boosting with data sampling techniques to form hierarchical ...
doi:10.35484/pssr.2021(5-i)38
fatcat:f5michv2ibhx3gh6qwrz5prhve
AN EFFECTIVE ARCHETYPE DESIGN OF HEART DISEASE ANTICIPATION USING OPTIMIZATION TECHNIQUES
2019
EPRA international journal of research & development
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Data mining plays a major role in the construction of an intellectual prediction model for healthcare system to detect Heart Disease (HD) using patient data sets, which support doctors in diminishing mortality ...
In this review, we focus the novel and unique aspects of cardiovascular disease health and the methodologies used to predict the CVD. ...
Indu Yekkala et
al[16]
2017
Prediction of Heart Disease
using Ensemble Learning
and Particle Swarm
Optimization
Various Ensembles
(Bagged tree, Random
Forest, and AdaBoost )
along with Feature ...
doi:10.36713/epra3747
fatcat:2eronako5zh55nhg6b4vlix6jm
Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation
[chapter]
2013
Lecture Notes in Computer Science
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This paper presents an experimental evaluation of eye gaze data as a source for modeling user's learning in Interactive Simulations (IS). ...
Our long-term goal is to build user models that can trigger adaptive support for students who do not learn well with ISs, caused by the often unstructured and open-ended nature of these environments. ...
The data was collected from a user study with 45 computer science students. ...
doi:10.1007/978-3-642-38844-6_18
fatcat:aagxtm74lrdwtas24wsolankcq
An Ensemble Prediction System Based on Artificial Neural Networks and Deep Learning Methods for Deterministic and Probabilistic Carbon Price Forecasting
2021
Frontiers in Environmental Science
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This research proposes an ensemble prediction system (EPS) that includes improved data feature extraction technology, three prediction submodels (GBiLSTM, CNN, and ELM), and a multiobjective optimization ...
The experimental results show that the ensemble prediction system can provide more effective and stable carbon price forecasting information and that it can provide valuable suggestions that enterprise ...
Data Preprocessing The data processing module includes the data feature extraction method, which is based on improved complex ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and the ...
doi:10.3389/fenvs.2021.740093
fatcat:w7twiqed6vggtea6ok6jux7xbe
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