Combination of loss functions for robust breast cancer prediction

Hajiabadi, Hamideh; Babaiyan, Vahide; Zabihzadeh, Davood; Hajiabadi, M Rahimi

doi:10.1016/j.compeleceng.2020.106624

Cited by 31 publications

(13 citation statements)

References 19 publications

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“…The CNN was noted as the second-best model with 96% accuracy, and the RNN was the third best model. The reason for the excellent performance of the ANN is that it is a very robust method against noise in training data and can implicitly reveal complex nonlinear relationships between dependent and independent variables [24] .…”

Section: Resultsmentioning

confidence: 99%

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

Mohammedqasem¹,

Mohammedqasim²,

Ata³

2022

Computers and Electrical Engineering

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Section: Resultsmentioning

confidence: 99%

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

Mohammedqasem¹,

Mohammedqasim²,

Ata³

2022

Computers and Electrical Engineering

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“…They used precision, recall, f1-Score, and accuracy to evaluate the performance of the proposed objective function. However, the new method was evaluated by doing experiments on Wisconsin Breast Cancer Diagnosis (WBCD) dataset, which is not the case for our study [7].…”

Section: Existing Literaturementioning

confidence: 99%

“…To have a well understanding of how our proposed approach behaves well, each metric of performance described above is computed using the formula in Equation (7).…”

Section: True Negative (Tn)mentioning

confidence: 99%

A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification

et al. 2021

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<div id="titleAndAbstract"><p class="0abstract">Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction performance of such diseases. The paper suggests a new hybrid approach-based gene selection that combines the filter method and the Ant Colony Optimization algorithm to find the smallest subset of informative genes (genes markers) among 24,481 genes. The proposed approach combines four machine learning algorithms - C5.0 Decision Tree, Support Vector Machines, K-Nearest Neighbors algorithm, and Random Forest Classifier - to classify each of the selected samples (patients) into two classes which have cancer or not. Compared with existing methods in the literature, experimental results indicate that our proposed gene selection approach achieved globally higher classification accuracies with a relatively smaller number of genes.</p></div>

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“…The smaller the loss function is, the better the robustness of the model will be. Common loss functions include 0-1 loss function, the log logarithmic loss function namely cross entropy loss function based on minimizing the negative likelihood, the squared loss function based on Ordinary further Squares, the absolute value loss function emphasized on the degree of deviation, the index loss function based on the Adaboost algorithm, Hinge loss function based on the support vector machine (SVM) [53], etc. They have different strengths and weaknesses.…”

Section: ) Loss Functionmentioning

confidence: 99%

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

Yang

2020

IEEE Access

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Combination of loss functions for robust breast cancer prediction

Cited by 31 publications

References 19 publications

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

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