Multi-class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier

Abdullah, Mohammad; Yap, Bee Wah; Sapri, Nik Nur Fatin Fatihah; Yaacob, Wan Fairos Wan

doi:10.1007/978-981-99-0741-0_24

Cited by 2 publications

(2 citation statements)

References 21 publications

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Section: Literature Reviewmentioning

confidence: 99%

“…The existing literature [ 23 , 24 , 25 ] explores various techniques for performing multi-class classification tasks with many conditional attributes. One of the studies [ 23 ] demonstrated that the support vector machine is an effective method for classifying multi-class breast cancer data with high dimensionality. The study compared the performance of Support Vector Machine with other methods, such as Naive Bayes, Random Forest, and multinomial Logistic Regression, and showed that the latter methods are prone to overfitting in this scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

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Ensemble Classifier Based on Interval Modeling for Microarray Datasets

Bentkowska,

Gałka,

Mrukowicz

et al. 2024

Entropy

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The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier. Moreover, orders for intervals are applied to determine the decision class of an object. The applied interval-valued aggregation functions are tested in terms of optimizing the performance of the considered ensemble classifier. The proposed model’s quality, superior to other well-known and component classifiers, is validated through comparison, demonstrating the efficacy of cross-entropy in ensemble model construction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Classifier Based on Interval Modeling for Microarray Datasets

Bentkowska,

Gałka,

Mrukowicz

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

Feature selection using differential evolution for microarray data classification

Prajapati,

Das,

Gourisaria

2023

Discov Internet Things

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The dimensions of microarray datasets are very large, containing noise and redundancy. The problem with microarray datasets is the presence of more features compared to the number of samples, which adversely affects algorithm performance. In other words, the number of columns exceeds the number of rows. Therefore, to extract precise information from microarray datasets, a robust technique is required. Microarray datasets play a critical role in detecting various diseases, including cancer and tumors. This is where feature selection techniques come into play. In recent times, feature selection (FS) has gained significant importance as a data preparation method, particularly for high-dimensional data. It is preferable to address classification problems with fewer features while maintaining high accuracy, as not all features are necessary to achieve this goal. The primary objective of feature selection is to identify the optimal subset of features. In this context, we will employ the Differential Evolution (DE) algorithm. DE is a population-based stochastic search approach that has found widespread use in various scientific and technical domains to solve optimization problems in continuous spaces. In our approach, we will combine DE with three different classification algorithms: Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). Our analysis will include a comparison of the accuracy achieved by each algorithmic model on each dataset, as well as the fitness error for each model. The results indicate that when feature selection was used the results were better compared to the results where the feature selection was not used.

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Multi-class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier

Cited by 2 publications

References 21 publications

Ensemble Classifier Based on Interval Modeling for Microarray Datasets

Ensemble Classifier Based on Interval Modeling for Microarray Datasets

Feature selection using differential evolution for microarray data classification

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