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Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Abdulfatai Ganiyu Oladepo, Amos Orenyi Bajeh, Abdullateef Oluwagbemiga Balogun, Hammed Adeleye Mojeed, Abdulsalam Abiodun Salman, Abdullateef Iyanda Bako
2021 International Journal of Interactive Mobile Technologies  
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

Shahzeb Haider, Adnan Akhunzada, Iqra Mustafa, Tanil Bharat Patel, Amanda Fernandez, Kim-Kwang Raymond Choo, Javed Iqbal
2020 IEEE Access  
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

Kudakwashe Zvarevashe, Oludayo Olugbara
2020 Algorithms  
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

Mustafa Jan, Akber A Awan, Muhammad S Khalid, Salman Nisar
2018 Research Reports in Clinical Cardiology  
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

Abdulhamit Subasi, Bayader Kadasa, Emir Kremic
2020 Procedia Computer Science  
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

Khan Md. Hasib, Md. Sadiq Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md. Imran Hossain Showrov, Shakil Ahmed, Obaidur Rahman
2020 Journal of Computer Science  
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

Kyungmi Lee
2010 Journal of Computers  
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

Sapiah Sakri, Ala Saleh
2020 International Journal of Advanced Computer Science and Applications  
, 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

Marion O. Adebiyi, Micheal O. Arowolo, Oludayo Olugbara
2021 Bulletin of Electrical Engineering and Informatics  
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

Faseeha Matloob, Taher M. Ghazal, Nasser Taleb, Shabib Aftab, Munir Ahmad, Muhammad Adnan Khan, Sagheer Abbas, Tariq Rahim Soomro
2021 IEEE Access  
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

Lean Yu, Lihang Yu, Kaitao Yu
2021 Financial Innovation  
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

Ghulam Mustafa
2021 Pakistan Social Sciences Review  
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

Deepika P, Saranya S, Dr.Sasikala S
2019 EPRA international journal of research & development  
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]

Samad Kardan, Cristina Conati
2013 Lecture Notes in Computer Science  
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

Yi Yang, Honggang Guo, Yu Jin, Aiyi Song
2021 Frontiers in Environmental Science  
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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