Hybrid Flower Pollination Algorithm and Support Vector Machine for Breast Cancer Classification

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Salehuddin, Roselina; Mustaffa, Noorfa Haszlinna

doi:10.30880/jtmb.2018.05.01.006

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Binary black hole algorithm with random forest (BBHA‐RF) works with the black hole phenomenon 24 . The flower pollination algorithm (FPA) works on the concept of pollination done by the pollens from the flowers as a process of reproduction 25 . The bat algorithm (BA) works on the process of bats searching for its prey using the echolocation strategy 26 .…”

Section: Introductionmentioning

confidence: 99%

Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

Seetharaman

Sundersingh

2021

Concurrency and Computation

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DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi‐objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre‐convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10‐fold cross‐validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi‐objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre‐convergence with suboptimal solutions in optimization algorithms is overcome.

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Section: Introductionmentioning

confidence: 99%

Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

Seetharaman

Sundersingh

2021

Concurrency and Computation

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Data preprocessing in knowledge discovery in breast cancer: systematic mapping study

Chlioui

Idri

Abnane

2020

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets

Alsuliman

Al-Baity

2022

Applied Sciences

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Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of individuals may be one of these factors, in addition to personal and behavioral characteristics (PBC). Machine learning (ML) based on PBC and GE data analytics emphasizes the need to develop accurate prediction models. The quality of prediction relies on the accuracy of the ML model. To improve the accuracy of prediction, optimized feature selection algorithms are applied to solve the high dimensionality problem of the datasets used. Comparing different optimized feature selection methods using bio-inspired algorithms over different types of data can allow for the most accurate model to be identified. Therefore, in this paper, we investigated enhancing the classification process of autism spectrum disorder using 16 proposed optimized ML models (GWO-NB, GWO-SVM, GWO-KNN, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, FPA-DT, BA-NB, BA-SVM, BA-KNN, BA-DT, ABC-NB, ABC-SVM, ABV-KNN, and ABC-DT). Four bio-inspired algorithms namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC), were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models. Five evaluation metrics were used to evaluate the performance of the proposed models: accuracy, F1 score, precision, recall, and area under the curve (AUC). The obtained results demonstrated that the proposed models achieved a good performance as expected, with accuracies of 99.66% and 99.34% obtained by the GWO-SVM model on the PBC and GE datasets, respectively.

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Hybrid Flower Pollination Algorithm and Support Vector Machine for Breast Cancer Classification

Cited by 3 publications

References 11 publications

Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

Data preprocessing in knowledge discovery in breast cancer: systematic mapping study

Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets

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