Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Zakrani, Abdelali; Hain, Mustapha; Idri, Ali

doi:10.11591/ijai.v8.i4.pp399-410

Cited by 16 publications

(22 citation statements)

References 46 publications

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“…As we can see from our previous result that support vector machine (SVM) and KNeighbors Classifiers achieved good accuracy when we minimized the number of features from 173 to 20. Since SVM and KNeighbors classifiers are computationally expensive due to the implementation of quadratic programming and require more time to execute classification [34,35], so reducing features helps in reducing the computation process and improving accuracy as well [37]. However, when we used probabilistic classifiers such as Naïve Bayes, the performance of accuracy is also improved with a 90.4 % rate using 10 features.…”

Section: Discussionmentioning

confidence: 99%

Exploring permissions in android applications using ensemble-based extra tree feature selection

al-kaaf

Ali

Shamsuddin

et al. 2020

IJEECS

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<span>The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used dataset had 25458 samples (8643 malware apps & 16815 benign apps) with 173 features. Three dataset with 25458 samples and 5, 10 and 20 features respectively were generated after using the proposed feature selection method. All the dataset was fed to machine learning. Support Vector machine (SVM), K Neighbors Classifier, Decision Tree, Naïve bayes and Multilayer Perceptron (MLP) classifiers were used. The classifiers models were evaluated using true negative rate (TNR), false positive rate (FNR) and accuracy metrics. The experimental results obtained showed that Support Vector machine and KNeighbors Classifiers with 20 features achieved the highest accuracy with 94 % and TNR with rate of 89 % using KNeighbors Classifier. The FNR rate is dropped to 0.001 using 5 features with support vector machine (SVM) and Multilayer Perceptrons (MLP) classifiers. The result indicated that reducing permission features improved the performance of classification and reduced the computational overhead.</span>

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Section: Discussionmentioning

confidence: 99%

Exploring permissions in android applications using ensemble-based extra tree feature selection

al-kaaf

Ali

Shamsuddin

et al. 2020

IJEECS

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“…Which these changes will affect the actual plan and schedule for a project estimation, leading to providing inaccurate plan which will cause a delay on the actual delivery date. Having such this negative impact can cause critical risks [24].…”

Section: -No Changing Accepted In Working Sprintsmentioning

confidence: 99%

“Scrumbear” framework for solving traditional scrum model problems

et al. 2021

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Software engineering is a discipline that is little understood by people. It defines how software is developed and maintained to meet the clients’ requirements. Software engineers follow certain systems and standards in their work to meet the clients’ desires. It is on this background that engineers follow specific models in coming up with the final product. One of the models highly used is scrum, which is one of the agile methodologies. However, despite being highly used, it has inherent flaws that need to be corrected. Those flaws are product owner continues changing; do not accept changes in working scrum, sprint’s release time limitation, finally wasting team time within each sprint. This paper presents a new framework, which is an updated version of the current Scrum, to overcome the scum models mentioned issues. In this study, a new framework is presented in a way that is understandable and needed by software developer’s team upon the collected qualitative and quantitative data. The implementation was by making some changes to the current scrum model leading to the “Scrumbear”, certain flaws can be corrected. One of the presented changes involve adding the control master rule to ensure controlling the requirements changing.

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“…After this study's experiment, we used three methods to discover the most influential attributes-namely, correlation-based, information-gain, and relief. While the correlation-based feature selection is a greedy search method, the others are rank-based search methods [22]. By using these methods, we have identified ten attributes as the most influential features.…”

Section: Feature Selection Algorithmsmentioning

confidence: 99%

Identification of important features and data mining classification techniques in predicting employee absenteeism at work

Al-Rasheed

2021

IJECE

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Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).

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Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Cited by 16 publications

References 46 publications

Exploring permissions in android applications using ensemble-based extra tree feature selection

Exploring permissions in android applications using ensemble-based extra tree feature selection

“Scrumbear” framework for solving traditional scrum model problems

Identification of important features and data mining classification techniques in predicting employee absenteeism at work

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