Parameter tuning in KNN for software defect prediction: an empirical analysis

Mabayoje, Modinat A.; Balogun, Abdullateef Oluwagbemiga; Jibril, Hajarah Afor; Atoyebi, Jelili Olaniyi; Mojeed, Hammed A.; Adeyemo, Victor Elijah

doi:10.14710/jtsiskom.7.4.2019.121-126

Cited by 26 publications

(14 citation statements)

References 28 publications

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“…It involves the deployment of machine learning (ML) methods on software features or metrics derived from software systems repositories to predict the quality and reliability of a software system [1,2]. These software features are the quantifiable attributes of software system that can be analyzed to ascertain software systems quality and reliability [3,4]. Knowledge gained from SDP processes can be used by software engineers for improving software development processes and managing limited software resources.…”

Section: Introductionmentioning

confidence: 99%

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

et al. 2020

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Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.

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Section: Introductionmentioning

confidence: 99%

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

et al. 2020

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“…Kajian penggunaan algoritme kNN tersebut menunjukkan bahwa kemampuan kNN sangat tergantung pada nilai k yang digunakan. Beragam kajian mengkaji bagaimana menentukan nilai k terbaik, seperti dalam [4], [11]- [13]. Namun, penentuan nilai k masih trivial dan sangat tergantung pada dataset yang digunakan.…”

Section: Pendahuluanunclassified

“…Namun, bias yang mendekati 0 akan menimbullkan dugaan terjadi overfitting seperti dinyatakan [24]. Nilai k yang terbaik juga bervariasi, tidak terlalu dekat dengan akar dari jumlah sampel seperti disebutkan dalam [1], namun lebih bersifat trivial atau uji coba seperti dalam [11].…”

Section: A Pengaruh Variasi Nilai K Dan Lag Untuk Prediksi 1 Bulan Bunclassified

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Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates

Akbar

Kusumodestoni

2020

Jurnal Teknologi dan Sistem Komputer

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Hotel occupancy rates are the most important factor in hotel business management. Prediction of the rates for the next few months determines the manager's decision to arrange and provide all the needed facilities. This study performs the optimization of lag parameters and k values of the k-Nearest Neighbor algorithm on hotel occupancy history data. Historical data were arranged in the form of supervised training data, with the number of columns per row according to the lag parameter and the number of prediction targets. The kNN algorithm was applied using 10-fold cross-validation and k-value variations from 1-30. The optimal lag was obtained at intervals of 14-17 and the optimal k at intervals of 5-13 to predict occupancy rates of 1, 3, 6, 9, and 12 months later. The obtained k-value does not follow the rule at the square root of the number of sample data.

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“…SDP models are built on details from software features such as source code complexity, software development history, software cohesion and coupling to predict defective modules in software systems. These software features are numerically quantified to determine the level of software systems quality and reliability [ 15 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

Balogun

Lafenwa-Balogun

Mojeed

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Class imbalance is a prevalent problem in machine learning which affects the prediction performance of classification algorithms. Software Defect Prediction (SDP) is no exception to this latent problem. Solutions such as data sampling and ensemble methods have been proposed to address the class imbalance problem in SDP. This study proposes a combination of Synthetic Minority Oversampling Technique (SMOTE) and homogeneous ensemble (Bagging and Boosting) methods for predicting software defects. The proposed approach was implemented using Decision Tree (DT) and Bayesian Network (BN) as base classifiers on defects datasets acquired from NASA software corpus. The experimental results showed that the proposed approach outperformed other experimental methods. High accuracy of 86.8% and area under operating receiver characteristics curve value of 0.93% achieved by the proposed technique affirmed its ability to differentiate between the defective and non-defective labels without bias.

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Parameter tuning in KNN for software defect prediction: an empirical analysis

Cited by 26 publications

References 28 publications

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

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