Effective feature set construction for SVM-based hot method prediction and optimisation

Johnson, Sandra; Shanmugam, Valli

doi:10.1504/ijcse.2011.042023

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Choosing the feature selection algorithm often requires expert knowledge as it is not an easy task to determine a good set of features. Basically, there are two general methods namely filters and wrappers (Johnson and Shanmugam, 2011) that currently being used in many feature-selection processes.…”

Section: Feature Selectionmentioning

confidence: 99%

Hybrid feature selection technique for intrusion detection system

Kamarudin

Maple

Watson

2019

IJHPCN

View full text Add to dashboard Cite

High dimensionality's problems have make feature selection as one of the most important criteria in determining the efficiency of intrusion detection systems. In this study we have selected a hybrid feature selection model that potentially combines the strengths of both the filter and the wrapper selection procedure. The potential hybrid solution is expected to effectively select the optimal set of features in detecting intrusion. The proposed hybrid model was carried out using correlation feature selection (CFS) together with three different search techniques known as best-first, greedy stepwise and genetic algorithm. The wrapper-based subset evaluation uses a random forest (RF) classifier to evaluate each of the features that were first selected by the filter method. The reduced feature selection on both KDD99 and DARPA 1999 dataset was tested using RF algorithm with tenfold cross-validation in a supervised environment. The experimental result shows that the hybrid feature selections had produced satisfactory outcome.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

Hybrid feature selection technique for intrusion detection system

Kamarudin

Maple

Watson

2019

IJHPCN

View full text Add to dashboard Cite

show abstract

“…In classification, SVM works as a discriminative classifier means it segregates the binary class data into two classes. SVM learns from a given inputs called the 'training data' which are classified with the expected output [41]. Unlike other classifiers, SVM tries to find the best separating line for the two given data sets.…”

Section: G Classification By Svmmentioning

confidence: 99%

Machine Learning based Test Case Prioritization in Object Oriented Testing

Singh¹,

Bhatia²,

Singhrova³

2019

IJRTE

View full text Add to dashboard Cite

Software maintenance is one of the most expensive activities in software life cycle. It costs nearly 70% of the total cost of the software. Either to adopt the new requirement or to correct the functionality, software undergoes maintenance. As a consequent of maintenance activities, software undergoes many reforms. Newly added software components may affect the working of existing components and also may introduce faults in existing components. The regression testing tries to reveal the faults that might have been introduced due to these reformations. Running all the prior existing test cases may not be feasible due to constraints like time, cost and resources. Test case prioritization may help in ordered execution of test cases. Running a faulty or fault prone component early in testing process may help in revealing more faults per unit of time. And hence may reduce the testing time. There have been many different criteria for assigning the priority to test cases. But none of the approaches so far have considered the object oriented design metrics for determining the priority of test cases. Object oriented design metrics have been empirically studied for their impact of software maintainability, reliability, testability and quality but usage of these metrics in test case prioritization is still an open area of research. The research reported in this paper evaluates subset of CK metrics. Metrics considered from CK suite include Coupling between objects (CBO), Depth of Inheritance tree (DIT), weighted methods per class (WMC), Number of children (NOC), and Response for a class (RFC). Study also considers four other metrics namely publically inherited methods (PIM), weighted attributes per class (WAC), number of methods inherited (NMI) and number of methods overridden. A model is built based on these metrics for the prediction of software quality and based on the quality measures software modules are classified with the help of Support Vector Machine (SVM) algorithm. The proposed approach is implemented in WEKA tool and analysed on experimental data extracted from open source software. Proposed work would firstly help the tester in identifying the low quality modules and then prioritize the test cases based on quality centric approach. The work also attempts to automate test case prioritization in object oriented testing. The results obtained are encouraging.

show abstract

“…For SVM early warning model established in this paper, one learning sample should consist of values of the ten early warning indicators and the corresponding warning level. The training module and prediction module of SVM model are programmed based on the mathematical principle of SVM (Ahn et al, 2011;Min and Lee, 2005;Chen et al, 2012;Johnson and Shanmugam, 2011;Lifeng et al, 2012;. At the beginning, the SVM model was established using only field samples.…”

Section: Establishment Of Early Warning Model Based On Svmmentioning

confidence: 99%

An intelligent production fluctuation monitoring system for giant oilfield development

Fang

Jiang

et al. 2017

IJES

View full text Add to dashboard Cite

Abstract:To guarantee production stability of oilfields, an intelligent production fluctuation monitoring and early warning system is developed based on methodologies of fuzzy comprehensive evaluation (FCE) and support vector machine (SVM). A novel early warning indicator system established through deep analysis of historical production data from Shengli oilfield is adopted in the FCE model and SVM model. Through performing field test, both the two models are proved to be capable of accurately predicting abnormal production decline of oilfields and the SVM model is proved to be of high prediction accuracy of 94%. Uncertainty analysis of early warning results can be realised in the system thanks to the mutual examination of the two models. This system also integrates modules of data reading, data processing, and result displaying, which facilitates application of it. The production monitoring and early warning system developed in this paper has been successfully applied in Shengli oilfield which is the second largest oilfield in China. It can play important role in ensuring production stability in oilfields, especially for giant oilfields and oilfields in high water-cut development stage.

show abstract

Effective feature set construction for SVM-based hot method prediction and optimisation

Cited by 6 publications

References 29 publications

Hybrid feature selection technique for intrusion detection system

Hybrid feature selection technique for intrusion detection system

Machine Learning based Test Case Prioritization in Object Oriented Testing

An intelligent production fluctuation monitoring system for giant oilfield development

Contact Info

Product

Resources

About