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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).  ...  The results show, BMB is a powerful ensemble solution for identifying minority examples in a text corpus.  ...  For the research articles corpus, where the data is imbalanced the ensemble learning algorithm may not be effective for the minority class.  ... 
doi:10.35484/pssr.2021(5-i)38 fatcat:f5michv2ibhx3gh6qwrz5prhve

Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced software defect data

Haitao He, Xu Zhang, Qian Wang, Jiadong Ren, Jiaxin Liu, Xiaolin Zhao, Yongqiang Cheng
2019 IEEE Access  
In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD.  ...  Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance.  ...  imbalanced data and that the integrated model can better adapt to the software defect data set.  ... 
doi:10.1109/access.2019.2934128 fatcat:xgdify3rzjg7lkijabv2eud7qq

Improving Voting Feature Intervals for Spatial Prediction of Landslides

Binh Thai Pham, Tran Van Phong, Mohammadtaghi Avand, Nadhir Al-Ansari, Sushant K. Singh, Hiep Van Le, Indra Prakash, Zheng-zheng Wang
2020 Mathematical Problems in Engineering  
and MultiBoost for landslide susceptibility assessment and prediction.  ...  In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost  ...  MultiBoost. MultiBoost is one of the ensemble learning methods developed by combining two ensemble learning algorithms, namely, AdaBoost and Wagging [56] [57] [58] .  ... 
doi:10.1155/2020/4310791 fatcat:kdtdnh4xnnhhvhza7tezvr4vre

Predicting asthma control deterioration in children

Gang Luo, Bryan L. Stone, Bernhard Fassl, Christopher G. Maloney, Per H. Gesteland, Sashidhar R. Yerram, Flory L. Nkoy
2015 BMC Medical Informatics and Decision Making  
We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence.  ...  We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead.  ...  Acknowledgments We thank Intermountain Allergy & Asthma for sharing their pollen count and mold level data, and Tom H. Greene and Xiaoming Sheng for helpful discussions. Drs.  ... 
doi:10.1186/s12911-015-0208-9 pmid:26467091 pmcid:PMC4607145 fatcat:cw3bi4nax5bydpn3gsdlsp5oja

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Gillala Rekha, Amit Kumar Tyagi, V. Krishna Reddy
2019 Journal of Computer Science  
If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance.  ...  level and ensemble or hybrid methods.  ...  The authors would like to thank Koneru Lakshmaiah Education Foundation and AARIN, India, an education foundation body and a research network for supporting the project through its financial assistance.  ... 
doi:10.3844/jcssp.2019.886.929 fatcat:cg3x36g4rzhybi7xzca6rfyaqi

Classification Techniques for Intrusion Detection An Overview

P. Amudha, S. Karthik, S. Sivakumari
2013 International Journal of Computer Applications  
Christine Dartigue et al.[7] proposed a new data-mining based technique for intrusion detection using an ensemble of binary classifiers with feature selection and multiboosting.  ...  [15] described a data mining framework for adaptively building Intrusion Detection (ID) models.  ... 
doi:10.5120/13334-0928 fatcat:pcjh5osxvbdbzfdkmiq5f7svga

Predicting Abnormal Stock Return Volatility Using Textual Analysis of News ? A Meta-Learning Approach

Renġta Myškovġ, Petr Hġjek, Vladimĭr Olej
2018 Amfiteatru Economic  
Moreover, we use meta-learning approach to simulate the decision-making process of various investors.  ...  Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.  ...  The group of meta-learning algorithms Random committee, Bagging and Multiboosting performed well, especially on class 1 (with a high TP rate).  ... 
doi:10.24818/ea/2018/47/185 fatcat:sa56sf7dlfci3ig2acr4fhsyai

An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining

Musavir Hassan, Muheet Ahmed Butt, Majid Zaman
2021 International Journal of E-Health and Medical Communications (IJEHMC)  
Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful  ...  of imbalanced classes. κ is evaluated using eq.( 11 ).  ...  Therefore, f-measure is basically the harmonic mean of precision and recall and thus effective for handling the imbalanced data distribution in different classes. f-measure for each class is computed and  ... 
doi:10.4018/ijehmc.20211101.oa8 fatcat:qivuxgocazhlzktxynpy3g4xuy

An Early Detection of Asthma using BOMLA Detector

Md. Abdul Awal, Md. Shahadat Hossain, Kumar Debjit, Nafiz Ahmed, Rajan Dev Nath, GM Monsur Habib, Md. Salauddin Khan, Md. Akhtarul Islam, M. A. Parvez Mahmud
2021 IEEE Access  
The weighted distribution for the minority classes is used according to their difficulty learning to generate synthetic data.  ...  Therefore, there is an imbalanced nature in the dataset where the asthma positive class is immensely higher than the asthma negative class.  ...  He was involved in teaching engineering subjects in the electrical, biomedical and mechatronics engineering courses at the School of Engineering, Macquarie University, for more than 2 years.  ... 
doi:10.1109/access.2021.3073086 fatcat:7bd4bo7qojgtzdzunkvwnqiapq

A Review of Ensemble Methods in Bioinformatics

Pengyi Yang, Yee Hwa Yang, Bing B. Zhou, Albert Y. Zomaya
2010 Current Bioinformatics  
Ensemble learning is an intensively studies technique in machine learning and pattern recognition.  ...  Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures  ...  Acknowledgement We thank Professor Joachim Gudmundsson for critical comments and constructive suggestions which have greatly improve the early version of this article.  ... 
doi:10.2174/157489310794072508 fatcat:muzcldjxifc23kl4tynz4lwjlu

Examining the impact of cross-domain learning on crime prediction

Fateha Khanam Bappee, Amilcar Soares, Lucas May Petry, Stan Matwin
2021 Journal of Big Data  
We choose ensemble learning methods for model building as it has generalization capabilities over new data.  ...  We create a uniform outline for Halifax, Nova Scotia, one of Canada's geographic regions, by adapting and learning knowledge from two different domains, Toronto and Vancouver, which belong to different  ...  Xiang Jiang for giving his valuable feedback on this work.  ... 
doi:10.1186/s40537-021-00489-9 pmid:34760434 pmcid:PMC8570338 fatcat:xbvu6he2ubh7vgjft3fuwejqam

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Bayu Adhi Tama, Sunghoon Lim
2021 Computer Science Review  
Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS.  ...  Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques  ...  Du and Zhang [51] applied a two-level selective ensemble learning algorithm for handling imbalanced datasets. Gormez et al.  ... 
doi:10.1016/j.cosrev.2020.100357 fatcat:6vojyshrd5aencao6hfgcpd2mm

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Michal Pavlicko, Marek Durica, Jaroslav Mazanec
2021 Mathematics  
Therefore, the main aim of the paper is ensemble model creation for financial distress prediction.  ...  Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models.  ...  Secondly, ensembles include multiboost J-48, multiboost-voted perceptron, multiboost logistic, bagging-J48, bagging logistic, and bagging-voted perceptron.  ... 
doi:10.3390/math9161886 fatcat:zmwih2vzzzgsnlvcjgy3ccl62a

CatBoost for big data: an interdisciplinary review

John T. Hancock, Taghi M. Khoshgoftaar
2020 Journal of Big Data  
CatBoost is a member of the family of GBDT machine learning ensemble techniques.  ...  Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their constructive evaluation of this paper, and the various members of the Data Mining and Machine Learning Laboratory, Florida  ... 
doi:10.1186/s40537-020-00369-8 pmid:33169094 pmcid:PMC7610170 fatcat:kf7jd7hbzvg2ni3zm2n3g5vraa

A Comparative Performance Assessment of Ensemble Learning for Credit Scoring

Yiheng Li, Weidong Chen
2020 Mathematics  
Experimental findings reveal that the performance of ensemble learning is better than individual learners, except for AdaBoost.  ...  With novel machine learning models continue to be proposed, ensemble learning has been introduced into the application of credit scoring, several researches have addressed the supremacy of ensemble learning  ...  Acknowledgments: The authors would like to thank the editors and anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.3390/math8101756 fatcat:ao63cyngnzfjraxsb45ocgottq
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