Search Word(s) in Title, Keywords, Authors, and Abstract: Bagging

Learning to Prevent Inactive Student of Indonesia Open University
Bayu Adhi Tama
Page: 165~172, Vol. 11, No.2, 2015
10.3745/JIPS.04.0015
Keywords: Educational Data Mining, Ensemble Techniques, Inactive Student, Open University
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The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.


Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Deepak Ghimire and Joonwhoan Lee
Page: 443~458, Vol. 10, No.3, 2014
10.3745/JIPS.02.0004
Keywords: Bagging, Ensemble Learning, Extreme Learning Machine, Facial Expression Recognition, Histogram of Orientation Gradient
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An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.


Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality
Ruchika Malhotra and Ankita Jain
Page: 241~262, Vol. 8, No.2, 2012
10.3745/JIPS.2012.8.2.241
Keywords: Empirical Validation, Object Oriented, Receiver Operating Characteristics, Statistical Methods, Machine Learning, Fault Prediction
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An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods