Gene selection with Game Shapley Harris hawks optimizer for cancer classification

Afreen, Sana; Bhurjee, Ajay Kumar; Aziz, Rabia Musheer

doi:10.1016/j.chemolab.2023.104989

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…This underscores the significance of selecting optimal model hyperparameters tailored to the specific data being analyzed. Considering the research outlined in [27]- [29], we can highlight the key performances of our proposed technique. In these prior studies, the authors developed effective methods for selecting informative attributes (genes) and applied both machine learning and deep learning techniques to identify various types of cancer.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

Babichev,

Liakh,

Kalinina

2024

IEEE Access

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This manuscript explores the application of deep learning (DL) techniques for classifying gene expression data. A key aspect of our research is the comparative analysis of various DL neural network architectures, including Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Recurrent Neural Networks (RNN), as well as hybrid models that combine these networks. We applied the Bayesian optimization algorithm using 5-fold cross-validation for optimal hyperparameter tuning, which is crucial for DL algorithm performance. Significantly, we have advanced the methods for applying RNNs in processing gene expression data, particularly focusing on LSTM and GRU types. Our study introduces also a novel hybrid quality criterion for data classification, calculated as a weighted sum of partial quality criteria, incorporating an integrated F1-score derived through the Harrington desirability method. Furthermore, we investigate hybrid models that leverage various DL methods, enhancing decision-making objectivity in sample identification. This model uses a step-by-step information processing procedure, initially applying different DL models to gene expression data and subsequently processing these through a CART-based classifier for final decision-making. Our experiments, performed on gene expression data from patients with eight cancer types and one subset with normal samples (without cancer), demonstrated that GRU-RNN-based models, particularly a two-layer GRU-RNN, achieved the highest classification efficacy, with an accuracy of 97.8% on the test dataset. The performance of this model exceeded that of other models, whose accuracy varied between 96.6% and 97.3%. Comparative analysis with other studies in this field suggests that the proposed techniques demonstrate higher efficacy compared to similar research regarding the application of DL models for cancer-type diagnosis.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

Babichev,

Liakh,

Kalinina

2024

IEEE Access

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“…In addition, we also want to explore the relationship between multiple diseases that occur in one fundus image in the future. At the same time, considering the existing issue of low accuracy, in future research directions, it may be beneficial to utilize classifiers such as Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (KNN) for the classification of DR and DME using features extracted through MaMNet [47].…”

Section: Discussionmentioning

confidence: 99%

A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading

Xing,

Mao,

Yan

et al. 2023

Applied Sciences

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Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively.

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“…The objective of feature selection is to enhance classification performance by selecting the most optimal set of features [52]. A trimmed dataset provides faster training times and greater accuracy.…”

Section: Feature Selectionmentioning

confidence: 99%

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

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The adoption of cloud computing has become increasingly widespread across various domains. However, the inherent security vulnerabilities of cloud computing pose significant risks to its overall safety. Consequently, intrusion detection systems (IDS) play a pivotal role in identifying malicious activities within a cloud system. The considerable volume of network traffic data may contain redundant and irrelevant features that can impact the classification performance of the classifier. In addition, the complexity and time consumption increase while processing such a substantial volume of data in the cloud intrusion detection process. To enhance the performance of the IDS, this study proposes a hybrid feature selection approach, combining two bio-inspired algorithms, namely the grasshopper optimization algorithm (GOA) and the genetic algorithm (GA). The combination of these two algorithms ensures a more efficient search for optimal solutions. A random forest (RF) classifier is trained using those optimal features. Moreover, the proposal addresses the challenge of imbalanced data by employing a hybrid approach: over-sampling the minority classes using an adaptive synthetic (ADASYN) algorithm, while implementing random under-sampling (RUS) for the majority class as needed. This integrated strategy significantly influences each category, enhancing the true positive rate (TPR) while minimizing the false positive rate (FPR), thus improving the overall system performance. The proposed approach was evaluated using three datasets: UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS EXF 2021. The recorded accuracies for these datasets were 98%, 99%, and 92%, respectively. The hybrid feature selection-based IDS demonstrated superior performance in multi-class classification, along with exemplary results for individual classes within the datasets. The proposed strategy exhibited a marked superiority with the random forest classifier, especially when compared to other classifiers including SVM, LR, FLN, LSTM, AlexNet, DNN, DBN, DT, and XGBoost. Moreover, this performance remained consistent and commendable even when benchmarked against contemporary state-of-the-art methodologies across multiple evaluation metrics.

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Gene selection with Game Shapley Harris hawks optimizer for cancer classification

Cited by 27 publications

References 44 publications

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

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