Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

Balogun, Abdullateef Oluwagbemiga; Basri, Shuib; Jadid, Said; Mahamad, Saipunidzam; Almomani, Malek Ahmad; Bajeh, Amos Orenyi; Alazzawi, Ammar K.

doi:10.1007/978-3-030-51965-0_43

Cited by 17 publications

(11 citation statements)

References 29 publications

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“…Alqushaibi et al [41] propose a new weight optimization method based on the sine cosine algorithm (SCA). Balogun et al [42] evaluate a number of different methods on a real-world dataset of software defects and show that they can significantly improve the performance of defect prediction models.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Sumiea,

Abdulkadir,

Ragab

et al. 2023

IEEE Access

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Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time. Precise estimation of hyperparameters during DRL training poses a major challenge. To tackle this problem, this study utilizes Grey Wolf Optimization (GWO), a metaheuristic algorithm, to optimize the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm for achieving optimal control strategy in two simulated Gymnasium environments provided by OpenAI. The ability to adapt hyperparameters accurately contributes to faster convergence and enhanced learning, ultimately leading to more efficient control strategies. The proposed DDPG-GWO algorithm is evaluated in the 2DRobot and MountainCarContinuous simulation environments, chosen for their ease of implementation. Our experimental results reveal that optimizing the hyperparameters of the DDPG using the GWO algorithm in the Gymnasium environments maximizes the total rewards during testing episodes while ensuring the stability of the learning policy. This is evident in comparing our proposed DDPG-GWO agent with optimized hyperparameters and the original DDPG. In the 2DRobot environment, the original DDPG had rewards ranging from -150 to -50, whereas, in the proposed DDPG-GWO, they ranged from -100 to 100 with a running average between 1 and 800 across 892 episodes. In the MountainCarContinuous environment, the original DDPG struggled with negative rewards, while the proposed DDPG-GWO achieved rewards between 20 and 80 over 218 episodes with a total of 490 timesteps.

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Section: Related Workmentioning

confidence: 99%

Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Sumiea,

Abdulkadir,

Ragab

et al. 2023

IEEE Access

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“…The 10-fold CV option is based on its ability to create malware detection models with the low impact of the issue of class imbalance [20,[50][51][52][53]. Moreover, the K-fold CV approach ensures that each instance can be used iteratively for both training and testing [54][55][56]. The Waikato Environment for Knowledge Analysis (WEKA) machine learning library [57] and R programming language [58] are used for the experimentation on an Intel(R) Core™ machine equipped with i7-6700, running at speed 3.4 GHz CPU with 16 GB RAM.…”

Section: 4mentioning

confidence: 99%

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Akintola¹,

Balogun

Mojeed³

et al. 2022

Int. J. Interact. Mob. Technol.

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Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.

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“…Proposed by Balogun Balogun et al (2020), 13 searchbased WFS methods were performed on 7 datasets for SDP with NB classification algorithm. Accuracy and AUC two evaluation metrics that were used to evaluate the performance of the SDP models.…”

Section: Feature Selectionmentioning

confidence: 99%

Feature selection for software defect prediction using an improved firefly algorithm

Cao

Ben

Zhang

et al. 2022

Preprint

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As all we know, the performance of the data-based learning models heavily depend on the quality of data that feature selection (FS) method is used to reduce the redundant features and irrelevant features to improve the classification performance. In this research, a feature selection method using an improved firefly algorithm (FA) is proposed to address the "curse-of-dimensionality" problem in software defect prediction (SDP). In the proposed method, where simulated annealing (SA) approach is used to avoid falling into local traps, a multi-group strategy is employed to obtain a promising optimal solution to the optimization problems, called the multi-group simulated annealing firefly algorithm (MSAFA). To evaluate the performance of MSAFA, excessive experiments have been conducted on a total of 28 optimization benchmark problems (CEC2013) and 6 class-imbalanced datasets selected from the NASA repository. In the comparison with other state-of-the-art swarm-based intelligence algorithms, MSAFA outperforms them to provide the optimal solutions to most optimization problems and SDP models. In short, the proposed feature selection method can obtain the promising solution to support data-based classified models in SDP

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Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

Cited by 17 publications

References 29 publications

Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Feature selection for software defect prediction using an improved firefly algorithm

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