@article {Hosni20212827,
	title = {A systematic mapping study for ensemble classification methods in cardiovascular disease},
	journal = {Artificial Intelligence Review},
	volume = {54},
	number = {4},
	year = {2021},
	note = {cited By 5},
	pages = {2827-2861},
	abstract = {Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature. {\textcopyright} 2020, Springer Nature B.V.},
	keywords = {Bayesian networks, Cardio-vascular disease, Cardiology, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Ensemble classifiers, Evaluation framework, K fold cross validations, Learning systems, Majority voting rules, Mapping, Open source software, Open systems, Optimization method, Systematic mapping studies},
	doi = {10.1007/s10462-020-09914-6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091735819\&doi=10.1007\%2fs10462-020-09914-6\&partnerID=40\&md5=69ea4b02de420c3ec6a85e1f3c7dddaf},
	author = {Hosni, M. and Carrillo de Gea, J.M. and Idri, A. and El Bajta, M. and Fern{\'a}ndez Alem{\'a}n, J.L. and Garc{\'\i}a-Mateos, G. and Abnane, I.}
}
@conference {ElBajta2020239,
	title = {Identifying software cost attributes of software project management in global software development: An integrative framework},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {239-244},
	abstract = {The management of global and distributed software projects is a very difficult task further complicated by the emergence of new challenges inherent in stakeholder dispersion. Software cost estimation plays a central role to face challenges in the context of Global Software Development (GSD). The objective of this study is to identify software cost attributes related to GSD context to present an integrative framework encompassing these attributes. Thirty cost attributes were identified using a Systematic Literature Review (SLR) and later compiled into a framework inspired by the Software Engineering Institute (SEI) taxonomy. {\textcopyright} 2020 ACM.},
	keywords = {Cost engineering, Cost estimating, Distributed software, global software development, Integrative framework, Intelligent systems, Project management, Software cost, Software cost estimations, Software design, Software engineering institutes, Software project management, Systematic literature review (SLR)},
	doi = {10.1145/3419604.3419780},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096416219\&doi=10.1145\%2f3419604.3419780\&partnerID=40\&md5=54b147fb2d591d8cef6a98304201c1e5},
	author = {El Bajta, M. and Idri, A.}
}
@conference {ElBajta201551,
	title = {Analogy-based software development effort estimation in global software development},
	booktitle = {Proceedings - 2015 IEEE 10th International Conference on Global Software Engineering Workshops, ICGSEW 2015},
	year = {2015},
	note = {cited By 0},
	pages = {51-54},
	abstract = {Context: Software development has always been characterised by certain parameters. In the case of global software development, one of the important challenges for software developers is that of predicting the development effort of a software system on the basis of developer details, size, complexity, and other measures. Objective: The main research topics related to global software development effort estimation are the definition and empirical evaluation of a search-based approach with which to build new estimation models and the definition and empirical evaluation of all available early data. Datasets have been used as a basis to carry out an analogy-based estimation using similarity functions and measures. Method: Many of the problems concerning the existing effort estimation challenges can be solved by creating an analogy. This paper describes an enhanced analogy-based model for the estimation of software development effort and proposes a new approach using similarity functions and measures for software effort estimation. Result: A new approach for analogy-based reasoning with which to enhance the performance of cost estimation in distributed or combined software projects dealing with numerical and categorical data. The proposed method will be validated empirically using The International Software Benchmarking Standards Group dataset as a basis. Conclusion: The proposed estimation model could be a useful approach for early stage effort estimation on distributed projects. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/ICGSEW.2015.19},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957927049\&doi=10.1109\%2fICGSEW.2015.19\&partnerID=40\&md5=c3b4560c70a9e611b029f6fcff6a71a3},
	author = {El Bajta, M.}
}
