A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets

Elzeki, O. M.; Alrahmawy, Mohammed F.; Elmougy, Samir

doi:10.5815/ijisa.2019.12.03

Cited by 9 publications

(6 citation statements)

References 22 publications

(18 reference statements)

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“…Table 2 shows the classification confusion matrix, including true positives, false positives, false negatives, and true negatives separately. As a result, a two-by-two confusion matrix (sometimes also referred to as a confusion matrix) was created [ 45 ]. By using the accuracy and the f1-measure, the classification performance could be studied in greater detail, as shown in Table 3 .…”

Section: 3 Active Learning Selection Strategiesmentioning

confidence: 99%

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

El‐Hasnony

Elzeki

Alshehri

et al. 2022

Sensors

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The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

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Section: 3 Active Learning Selection Strategiesmentioning

confidence: 99%

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

El‐Hasnony

Elzeki

Alshehri

et al. 2022

Sensors

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“…To address this sample imbalance problem, dynamic weighting coefficients of each category are incorporated into the DWSA to conduct the classification task. We use accuracy and F1-score [41] to evaluate the model. The dynamic sample weighting algorithm adjusts the weight coefficients of each category.…”

Section: Dwsa Frameworkmentioning

confidence: 99%

DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining

Yue

2021

Electronics

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The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important to information extraction and data mining. However, unlike the well-studied field of text semantic analysis, current work in document structural analysis is still scarce. In this paper, we propose an intelligent document structural analysis framework through data pre-processing, feature engineering, and structural classification with a dynamic sample weighting algorithm. As a typical application, we collect more than 11,000 insurance document content samples and carry out the machine learning experiments to check the efficiency of our framework. Meanwhile, to address the sample imbalance problem in the hierarchy classification task, a dynamic sample weighting algorithm is incorporated into our Dynamic Weighting Structural Analysis (DWSA) framework, in which the weights of different category tags according to the structural levels are iterated dynamically in training. Our results show that the DWSA has significantly improved the comprehensive accuracy and the classification F1-score of each category. The comprehensive accuracy is as high as 94.68% (3.36% absolute improvement) and the Macro F1-score is 88.29% (5.1% absolute improvement).

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“…The concept is based on the survival of the fittest strategy during sexual reproduction as proposed by Charles Darwin [14]. When dealing with finding optimal solutions and stochastic search [15], then genetic algorithm is the best bet. These algorithms relies on the combination of chromosome populations, selection, crossover, and mutation [16] to produce new offspring.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Credit Card Fraud Detection System Using Machine Learning

Makolo¹,

Adeboye²

2021

IJITCS

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The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

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A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets

Cited by 9 publications

References 22 publications

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining

Credit Card Fraud Detection System Using Machine Learning

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