Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Mafarja, Majdi; Thaher, Thaer; Al-Betar, Mohammed Azmi; Too, Jingwei; Awadallah, Mohammed A.; Doush, Iyad Abu; Turabieh, Hamza

doi:10.1007/s10489-022-04427-x

Cited by 37 publications

(17 citation statements)

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“…The results of the experiments showed that the proposed nestedstacking system outperformed the baseline models. In [15], the researchers proposed a framework based on the Binary Whale Optimization Algorithm (BWOA) called SBEWOA. The BWOA was used as an FS method that utilized transfer functions to convert the original continuous WOA into a binary version suitable for FS.…”

Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning

confidence: 99%

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Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.

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Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning

confidence: 99%

“…In recent years, feature selection methods have gained more prominence due to their capability to enhance prediction accuracy and reduce model creation time [15]- [19]. These methods find applications in various domains, including healthcare and medicine, fraud detection, sentiment analysis, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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“…A classification framework 35 is designed to identify faulty software efficiently. The approach is characterized by the integration of two key components: an enhanced exploratory Whale Optimization Algorithm (WOA) for feature selection and a Random Forest ensemble learning model for classification.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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“…For detecting and classifying the CKD, the AGRU model can be used. RNN is a network persisting data for sequence-related tasks [20]. But, RNN suffers from short-term memory.…”

Section: Classification Using Agru Modelmentioning

confidence: 99%

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

2024

IJIES

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Chronic Kidney Disease (CKD) is a progressive condition that may cause kidney failure, so earlier diagnosis is critical for proper management. The condition has a large fatality, particularly in developing nations. CKD often remains unnoticed because there is no obvious earlier-stage symptom. Meanwhile, earlier diagnosis and on-time clinical intervention are essential for reducing the disease progression. Detecting CKD using deep learning (DL) methods and feature selection (FS) could be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnoses (CAD) to assist clinicians in accomplishing earlier CKD recognition. Therefore, this study develops an automated CKD detection using Henry Gas Optimization Algorithm with DL (CKDD-HGSODL) approach. The drive of the CKDD-HGSODL approach is to classify and detect the presence of CKD utilizing FS and hyperparameter tuning strategies. In the presented CKDD-HGSODL technique, min-max scaling can be used to normalize the input data. In addition, the CKDD-HGSODL technique utilizes the HGSO model for selecting optimal features. For the CKD detection process, an attention-based gated recurrent unit (AGRU) model can be utilized. At last, the slime mould algorithm (SMA) can be utilized for the optimal hyperparameter selection of the AGRU approach that aids in improving the classifier results. To validate the outcome of the CKDD-HGSODL approach, a comprehensive simulation value is made on the benchmark CKD database. The obtained performances depict the enhanced detection results of the CKDD-HGSODL approach on CKD diagnosis.

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Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Cited by 37 publications

References 98 publications

Analysis of Feature Selection Methods in Software Defect Prediction Models

Analysis of Feature Selection Methods in Software Defect Prediction Models

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

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