Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

Mojrian, Sanaz; Pintér, Gergő; Joloudari, Javad Hassannataj; Felde, Imre; Nabipour, Narjes; Nádai, László; Mosavi, Amir

doi:10.20944/preprints201910.0349.v1

Cited by 10 publications

(8 citation statements)

References 34 publications

(8 reference statements)

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“…In this paper, the confusion matrix is used to evaluate the proposed models [37]- [39], [52]. This matrix includes 4 elements, including True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN).…”

Section: Methods Evaluationmentioning

confidence: 99%

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

et al. 2020

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One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure of a network. One of the solutions to detect a unanimous APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the system may crash due to the high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods of C5.0 decision tree, Bayesian network, and deep learning are used for the timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, a 10-fold cross-validation method is used to experiment with these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 95.64%, 88.37%, and 98.85%, respectively. Also, in terms of the critical criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 2.56, 10.47, and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false-negative rate, and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

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Section: Methods Evaluationmentioning

confidence: 99%

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

et al. 2020

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“…4 out of 7) and three models have been developed to address problems related to e-commerce, namely order arrival prediction, dynamic credit risk evaluation, and product recommendation. [6]. There is much evidence in the literature that ensemble classifiers and ensemble learning systems have been very effective in financial time series data.…”

Section: Hybrid Machine Learning Methodsmentioning

confidence: 99%

“…The evolution of DS methods has progressed at a fast pace, and every day, many new sectors and disciplines are added to the number of users and beneficiaries of DS algorithms. On the other hand, hybrid machine learning models consist of two or more single algorithms and are used to increase the accuracy of the other models [6,7]. Hybrid models can be formed by combining two predictive machine learning algorithms or a machine learning algorithm and an optimization method to maximize the prediction function [8].…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated that the hybrid machine learning models outperform the single algorithms, and such an approach has improved the prediction accuracy [9][10][11]. Ensemble machine learning algorithms are one of the supervised learning algorithms that use multiple learning algorithms to improve learning processes and increase predictive accuracy [6]. Ensemble models apply different training algorithms to enhance training and learning from data.…”

Section: Introductionmentioning

confidence: 99%

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Nosratabadi

Duan

Ghamisi

et al. 2020

Preprint

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This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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“…This made Chicco and Jurman [3] to use machine learning methods to predict survival of patients with heart failure in which their results showed that serum creatinine and ejection fraction alone are enough to predict mortality of heart failure. However, major challenges in model learning is the feature selection problem, as the feature selection step is so important in machine learning with the purpose of eliminating unnecessary and unimportant features [9,[14][15][16] and also dealing with imbalanced dataset. It is not always possible to generate a good predictive model for imbalance dataset; approaches have been proposed to this issue in which we try to use in this paper in order to address the problem.…”

Section: Introductionmentioning

confidence: 99%

Predicting Survival of Heart Failure Patients Using Classification Algorithms

Oladimeji

Oladimeji²

2020

j.inf.tech.comp.eng

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Heart failure is a situation that occurs when the heart is unable to pump enough blood to meet the needs of other organs in the body. It is responsible for the annual death of approximately 17 million people worldwide. Series of studies have been done to predict heart failure survival with promising results. Hence, the purpose of this study is to increase the accuracy of previous works on predicting heart failure survival by selecting significant predictive features in order of their ranking and dealing with class imbalance in the classification dataset. In this study, we propose an integrated method using machine learning. The proposed method shows promising results as it performs better than previous works and this study confirms that dealing with imbalanced dataset properly increases accuracy of a model. The model was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at predicting if a patient will survive heart failure. Attention may be focused on mainly serum creatinine, ejection fraction, smoking status and age.

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Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

Cited by 10 publications

References 34 publications

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Predicting Survival of Heart Failure Patients Using Classification Algorithms

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