Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests

Khadse, Vijay; Mahalle, Parikshit N.; Shinde, Gitanjali Rahul

doi:10.4018/ijaci.2020070105

Cited by 21 publications

(11 citation statements)

References 69 publications

(9 reference statements)

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“…measures. The statistical difference has been analyzed using Nemenyi as a posthoc test [53]. Detailed results can be fetched from 1 Table 12 lists the ranking of Ensembles for each performance measure.…”

Section: Approach 2: Feature Selection Resultsmentioning

confidence: 99%

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Kaur

2021

IEEE Access

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A. PURPOSECode smells are residuals of technical debt induced by the developers. They hinder evolution, adaptability and maintenance of the software. Meanwhile, they are very beneficial in indicating the loopholes of problems and bugs in the software. Machine learning has been extensively used to predict Code Smells in research. The current study aims to optimise the prediction using Ensemble Learning and Feature Selection techniques on three open-source Java data sets. B. DESIGN AND RESULTSThe work Compares four varied approaches to detect code smells using four performance measures Accuracy(P1), G-mean1 (P2), G-mean2 (P3), and F-measure (P4). The study found out that values of the performance measures did not degrade it instead of either remained same or increased with feature selection and Ensemble Learning. Random Forest turns out to be the best classifier while Correlation-based Feature selection(BFS) is best amongst Feature Selection techniques. Ensemble Learning aggregators, i.e. ET5C2 (BFS intersection Relief with classifier Random Forest), ET6C2 (BFS union Relief with classifier Random Forest), and ET5C1 (BFS intersection Relief with Bagging) and Majority Voting give best results from all the aggregation combinations studied. C. CONCLUSIONSThough the results are good, but using Ensemble learning techniques needs a lot of validation for a variety of data sets before it can be standardised. The Ensemble Learning techniques also pose a challenge concerning diversity and reliability and hence needs exhaustive studies.

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Section: Approach 2: Feature Selection Resultsmentioning

confidence: 99%

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Kaur

2021

IEEE Access

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“…Dalam penggunaan uji statistik parametrik dan non parametrik, perlu dilaksanakan uji persyaratan analisis. Pengujian dengan uji statistik inferensial parametrik mensyaratkan uji normalitas, uji homogenitas variansi, dan uji linearitas Khadse et al, (2020);Osma-Pinto et al, (2020). Langkah uji hipotesis statistik yang dapat dilakukan oleh seorang peneliti dalam melakukan uji persyaratan analisis, yakni: (1) rumuskan hipotesis H0 dan H1; (2) Tetapkan tingkat signifikansi α ; (3) Tetapkan daerah kritis atau daerah dimana H0 ditolak atau H1 diterima; (4) Tetapkan statistik uji; (5) Lakukan perhitungan; (6) Tarik Kesimpulan Emmert-Streib & Dehmer, (2019); Yuan et al, (2017).…”

Section: Pendahuluanunclassified

Uji homogenitas sebagai syarat pengujian analisis

Sianturi

2022

PSSA

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Uji homogenitas adalah prosedur uji statistik yang bertujuan untuk menunjukkan bahwa dua atau lebih kelompok sampel data diambil dari populasi yang memiliki varians yang sama. Jenis penelitian ini adalah penelitian kualitatif. Dalam penelitian kualitatif, peneliti tidak terjun langsung ke lapangan untuk mencari sumber data penelitian. Salah satu penelitian yang termasuk dalam penelitian kualitatif adalah penelitian kepustakaan. Pengujian persyaratan analitik merupakan konsep dasar untuk menentukan statistik uji mana yang diperlukan, apakah pengujian tersebut menggunakan statistik parametrik atau non parametrik. Uji syarat, yaitu uji homogenitas varians populasi dalam sebaran data penelitian. Uji homogenitas menggunakan statistik uji F, uji Bartlett, uji Levene, uji Cohran, dan uji Harley. Kesimpulan dari penelitian ini adalah betapa pentingnya pengujian syarat analisis ini, penulis menyarankan kepada peneliti untuk memperhatikan pengujian syarat analisis terlebih dahulu sebelum melakukan pengujian hipotesis, sehingga pengujian hipotesis yang kami ajukan adalah sesuai dengan uji statistik inferensial yang kita butuhkan dalam mengolah data hasil. belajar.

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“…There are some molecular docking (computational methods)-based methods that have been applied to FDA-approved drugs or antiviral drugs to identify useful molecules on viral proteins of SARS-CoV-2. But ML [ 88 ] based method can be a helpful tool. In this section, we propose to use our PreDTIs (ML-based DTI) model to identify the most potent drugs against SARS-CoV-2.…”

Section: Discover New Drugs To Treat Covid-19 Patientsmentioning

confidence: 99%

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

Mahmud

Chen

Liu

et al. 2021

Briefings in Bioinformatics

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Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

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Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests

Cited by 21 publications

References 69 publications

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Uji homogenitas sebagai syarat pengujian analisis

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

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