Correlation-based Feature Selection Strategy in Neural Classification

Michalak, Krzysztof; Kwaśnicka, Halina

doi:10.1109/isda.2006.128

Cited by 48 publications

(30 citation statements)

References 5 publications

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“…A good feature set contains features that are highly correlated with the change in the class but that are not dependent on each other [29]. CFS helps to find the unwanted and noisy features that are correlated with other features.…”

Section: Correlation-based Feature Selectionmentioning

confidence: 99%

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Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques

Malhotra¹,

Jangra²

2015

J Inf Process Syst

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Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

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Section: Correlation-based Feature Selectionmentioning

confidence: 99%

“…We utilized this method in our study for feature selection. The advantages of feature selection are reduction in dimensionality, improved predictive accuracy, and reduced execution time [28,29].…”

Section: Correlation-based Feature Selectionmentioning

confidence: 99%

Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques

Malhotra¹,

Jangra²

2015

J Inf Process Syst

View full text Add to dashboard Cite

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“…A broad spectrum of various wrappers is used in today's approaches. For example, the forward and the backward floating search and their combinations are commonly used, where one feature is added or reduced at a time, depending on the classification accuracy, but also some advanced methods based on the aforementioned ones which take into account feature dependencies (Michalak and Kwaśnicka, 2006).…”

Section: Classification and Feature Selectionmentioning

confidence: 99%

A differential evolution approach to dimensionality reduction for classification needs

Martinović

Bajer

Zorić

2014

International Journal of Applied Mathematics and Computer Science

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The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold crossvalidation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.

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“…The prediction models have been built using the commonly used regression and machine learning techniques used by researchers [4][5][6][7]. They also investigate a new group of bio-inspired techniques better known as Artificial Immune System (AIS) algorithms for their ability to detect change prone classes using inter project validation.…”

Section: Introductionmentioning

confidence: 99%

Applicability of Inter Project Validation for Determination of Change Prone Classes

Malhotra¹,

Gupta²,

Khanna³

2014

IJCA

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The research in the field of defect and change proneness prediction of software has gained a lot of momentum over the past few years. Indeed, effective prediction models can help software practitioners in detecting the change prone modules of a software, allowing them to optimize the resources used for software testing. However, the development of the prediction models used to determine change prone classes are dependent on the availability of historical data from the concerned software. This can pose a challenge in the development of effective change prediction models. The aim of this paper is to address this limitation by using the data from models based on similar projects to predict the change prone classes of the concerned software. This inter project technique can facilitate the development of generalized models which can be used to ascertain change prone classes for multiple software projects. It would also lead to optimization of critical time and resources in the testing and maintenance phases. This work evaluates the effectiveness of statistical and machine learning techniques for developing such models using receiver operating characteristic analysis. The observations of the study indicate varied results for the different techniques used.

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Correlation-based Feature Selection Strategy in Neural Classification

Cited by 48 publications

References 5 publications

Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques

Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques

A differential evolution approach to dimensionality reduction for classification needs

Applicability of Inter Project Validation for Determination of Change Prone Classes

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