Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification

Hamid, Tengku Mazlin Tengku Ab; Sallehuddin, Roselina; Yunos, Zuriahati Mohd; Ali, Aida

doi:10.1016/j.mlwa.2021.100054

Cited by 26 publications

(20 citation statements)

References 31 publications

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“…To measure the efficiency of the developed cancer prediction model, various experiments are performed. The performance of the developed model is compared with three very recent methods of filter-based cancer microarray data classification: Tabu Asexual Genetic Algorithm (TAGA) [57], Harmonize Particle Swarm Optimization (HPSO) [33], Least Loss (LL) [58].…”

Section: Resultsmentioning

confidence: 99%

“…The reported results show that in all cases the developed model performs better than the recently state-of-the-art approaches. For example, for the Lung Cancer dataset, the proposed method yields a 91.82% classification accuracy while TAGA [57], HPSO [33], and LL [58] reported 90.19%, 90.83%, and 89.61%, correspondingly.…”

Section: Resultsmentioning

confidence: 99%

“…Many of state-of-the art machine learningbased models in cancer prediction [24][25][26][27][28] used black-box like deep learning techniques with no explanation for their prediction. In this paper, a simple and easily interpreted decision system for [33,57,58], this study developed a model based on ensemble DF. This yields an increase in prediction accuracy and reduced the likelihood of overfitting.…”

Section: Novelties and Future Workmentioning

confidence: 99%

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Cancer prediction using graph-based gene selection and explainable classifier

Rostami

Oussalah

2022

FinJeHeW

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Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Novelties and Future Workmentioning

confidence: 99%

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Cancer prediction using graph-based gene selection and explainable classifier

Rostami

Oussalah

2022

FinJeHeW

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“…Particle swarm optimization (PSO) is an optimization algorithm based on a random probability distribution. Kennedy and Eberhart initially presented it in 1995 and also represented its various uses for different problem‐solving purposes 40,41 . The social behavior of fish schools and bird flocks, in which individuals coordinate their movement based on the positions and velocities of their neighbors, serves as the model for the algorithm.…”

Section: Methods Usedmentioning

confidence: 99%

“…Kennedy and Eberhart initially presented it in 1995 and also represented its various uses for different problem-solving purposes. 40,41 The social behavior of fish schools and bird flocks, in which individuals coordinate their movement based on the positions and velocities of their neighbors, serves as the model for the algorithm. Because of its simplicity, resilience, and efficacy in tackling a wide range of optimization problems, PSO has also grown in popularity for cancer detection.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

Joshi,

Aziz

2023

Int J Imaging Syst Tech

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This study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated tumors can be fatal. Despite advances in AI, accurately classifying tumors remains a challenging task. To address this challenge, we propose a novel optimization approach called PSCS combined with deep learning for brain tumor classification. PSCS optimizes the classification process by improving Particle Swarm Optimization (PSO) exploitation using Cuckoo search (CS) algorithm. Next, classified gene expression data of brain tumor using Deep Learning (DL) to identify different groups or classes related to a particular tumor along with the PSCS optimization technique. The proposed optimization technique with DL achieves much better classification accuracy than other existing DL and Machine learning models with the different evaluation matrices such as Recall, Precision, F1‐Score, and confusion matrix. This research contributes to AI‐driven brain tumor diagnosis and classification, offering a promising solution for improved patient outcomes.

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Multi-filter Wrapper Enhanced Machine Learning Model for Cancer Diagnosis

Sahu

Dash

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification

Cited by 26 publications

References 31 publications

Cancer prediction using graph-based gene selection and explainable classifier

Cancer prediction using graph-based gene selection and explainable classifier

Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

Multi-filter Wrapper Enhanced Machine Learning Model for Cancer Diagnosis

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