Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

Tumar, Iyad; Hassouneh, Yousef; Turabieh, Hamza; Thaher, Thaer

doi:10.1109/access.2020.2964321

Cited by 79 publications

(54 citation statements)

References 50 publications

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“…The latest advances in feature selection are a combination of feature selection with deep learning especially the Convolutional Neural Networks (CNN) for classification tasks, such as applications in bioinformatics neurodegenerative disorders classification using the Principal Components Analysis (PCA) algorithm [112,113], brain tumor segmentation [114] using three planar super pixel based statistical and textural features extraction. Next, remote sensing imagery classification using a fusion of CNN and RF [115], and software fault prediction [116] using enhanced binary moth flame optimization as a feature selection, and text classification based on independent feature space search [117].…”

Section: Evaluation Performance and Discussionmentioning

confidence: 99%

Selecting critical features for data classification based on machine learning methods

et al. 2020

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In machine learning problems, high dimensional data, especially in terms of many features, is increasingly these days [1]. Many researchers focus on the experiment to solve these problems. Besides, to extract important features from these high dimensional of variables and data. The statistical techniques were used to minimize noise and redundant data. Nevertheless, we do not use all the features to train a model. We may improve our model with the features correlated and non-redundant, so feature selection plays an important role.

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Section: Evaluation Performance and Discussionmentioning

confidence: 99%

Selecting critical features for data classification based on machine learning methods

et al. 2020

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“…The most commonly used SDLC models are waterfall, Agile and Spiral models. Empirically, testing is primarily concerned with the enhancement of the software quality and reducing the total cost [2]- [4]. The task of detecting or predicting faults in software is named SFP.…”

Section: Doamentioning

confidence: 99%

“…The task of detecting or predicting faults in software is named SFP. In the SFP process, prior to a new version of software being developed, hidden or clear fault-prone models can be detected with the help of user comments, historical fault datasets gathered from previous projects, or predefined software matrices [3], [4].…”

Section: Doamentioning

confidence: 99%

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Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

et al. 2021

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Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

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“…Reference [47] propose an improved version of gravitational search algorithm (GSA) by using the concept of global memory and the definition of exponential K best to solve the feature selection problems. Tumar et al [48] propose an enhanced binary moth flame optimization (EBMFO) with adaptive synthetic sampling (ADASYN) to predict the most optimal feature combination in software faults. More improved algorithms for feature selections can be found in literature [49], [50], [51] , [52], [53], [54], and [55].…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

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Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

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Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

Cited by 79 publications

References 50 publications

Selecting critical features for data classification based on machine learning methods

Selecting critical features for data classification based on machine learning methods

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

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