An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Balogun, Abdullateef Oluwagbemiga; Basri, Shuib; Capretz, Luiz Fernando; Mahamad, Saipunidzam; Imam, Abdullahi Abubakar; Almomani, Malek Ahmad; Adeyemo, Victor Elijah; Kumar, Ganesh

doi:10.3390/e23101274

Cited by 15 publications

(4 citation statements)

References 67 publications

(94 reference statements)

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“…Balogun et al [28] proposed the adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) approach to deal with the problem of large dimensionality and filter rank selection in SDP. The empirical outcome demonstrates the model's efficiency over existing approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Defect Prediction Based Ensemble Approach

Harikiran¹,

Chandana²,

Srinivasarao³

et al. 2023

Computer Systems Science and Engineering

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Software systems have grown significantly and in complexity. As a result of these qualities, preventing software faults is extremely difficult. Software defect prediction (SDP) can assist developers in finding potential bugs and reducing maintenance costs. When it comes to lowering software costs and assuring software quality, SDP plays a critical role in software development. As a result, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently. Several methods for detecting and addressing such flaws at a low cost have been offered. These approaches, on the other hand, need to be significantly improved in terms of performance. Therefore in this paper, two deep learning (DL) models Multilayer preceptor (MLP) and deep neural network (DNN) are proposed. The proposed approaches combine the newly established Whale optimization algorithm (WOA) with the complementary Firefly algorithm (FA) to establish the emphasized metaheuristic search EMWS algorithm, which selects fewer but closely related representative features. To find the best-implemented classifier in terms of prediction achievement measurement factor, classifiers were applied to five PROMISE repository datasets. When compared to existing methods, the proposed technique for SDP outperforms, with 0.91% for the JM1 dataset, 0.98% accuracy for the KC2 dataset, 0.91% accuracy for the PC1 dataset, 0.93% accuracy for the MC2 dataset, and 0.92% accuracy for KC3.

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Section: Literature Reviewmentioning

confidence: 99%

Software Defect Prediction Based Ensemble Approach

Harikiran¹,

Chandana²,

Srinivasarao³

et al. 2023

Computer Systems Science and Engineering

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“…In study [ 48 ] , a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in software defect prediction (SDP). Specifically, the AREMFFS method is based on evaluating and combining the strengths of individual filter feature selection (FFS) methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process.…”

Section: -3 Related Workmentioning

confidence: 99%

Weighted Rank Aggregation Based on Ranker Accuracies for Feature Selection

Abdolrazzagh-Nezhad¹,

Kherad

2023

Preprint

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Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The experiments show the superiority of the proposed method compared to the existing baseline FS methods and rank aggregation-based multi-filter FS methods.

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“…However, filter rank selection problem and complex search strategies are inherent limitations/drawbacks of HFS methods. In particular, selecting the most appropriate filter method for HFS is difficult, as the performance of FFS methods depends on the choice of datasets and classifiers [ 36 – 41 ]. Also, the local optima stagnation and high computational costs of WFS as a result of large search spaces are inherited by the HFS method [ 42 – 44 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Rank Aggregation-Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Balogun

Basri

Mahamad

et al. 2021

Computational Intelligence and Neuroscience

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The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods.

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An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Cited by 15 publications

References 67 publications

Software Defect Prediction Based Ensemble Approach

Software Defect Prediction Based Ensemble Approach

Weighted Rank Aggregation Based on Ranker Accuracies for Feature Selection

A Novel Rank Aggregation-Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

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An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Cited by 15 publications

References 67 publications

Software Defect Prediction Based Ensemble Approach

Software Defect Prediction Based Ensemble Approach

​Weighted Rank Aggregation Based on Ranker Accuracies for Feature Selection

A Novel Rank Aggregation-Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

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Product

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Weighted Rank Aggregation Based on Ranker Accuracies for Feature Selection