Ensemble Classifiers and Their Applications: A Review

Rahman, Akhlaqur; Tasnim, Sumaira

doi:10.14445/22312803/ijctt-v10p107

Cited by 50 publications

(32 citation statements)

References 46 publications

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“…This technique works especially well for unstable learning algorithms whose output classifier undergoes major changes in response to small changes in the training data: Decision tree, neural network, and rule learning algorithms are all unstable, linear regression, nearest neighbor, and linear threshold algorithms are generally very stable. Different network weights are used to train the base neural network learning process [11]. These methods achieve better generalization.…”

Section: Manipulation Of the Training Parametersmentioning

confidence: 99%

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Improving Student Enrollment Prediction Using Ensemble Classifiers

Wanjau¹,

Muketha²

2018

IJCATR

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Abstract:In the recent years, data mining has been utilized in education settings for extracting and manipulating data, and for establishing patterns in order to produce useful information for decision making. There is a growing need for higher education institutions to be more informed and knowledgeable about their students, and for them to understand some of the reasons behind students' choice to enroll and pursue careers. One of the ways in which this can be done is for such institutions to obtain information and knowledge about their students by mining, processing and analyzing the data they accumulate about them. In this paper, we propose a general framework for mining student data enrolled in Science, Technology, Engineering and Mathematics (STEM) using performance weighted ensemble classifiers. We train an ensemble of classification models from enrollment data streams to improve the quality of student data by eliminating noisy instances, and hence improving predictive accuracy. We empirically compare our technique with single model based techniques and show that using ensemble models not only gives better predictive accuracies on student enrollment in STEM, but also provides better rules for understanding the factors that influence student enrollment in STEM disciplines.

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Section: Manipulation Of the Training Parametersmentioning

confidence: 99%

“…In this case, an error is imposed if base classifiers make identical errors on similar patterns [11]. An example of such an ensemble is the Negative correlation learning.…”

Section: Manipulation Of the Error Functionmentioning

confidence: 99%

Improving Student Enrollment Prediction Using Ensemble Classifiers

Wanjau¹,

Muketha²

2018

IJCATR

View full text Add to dashboard Cite

show abstract

“…Usually diversity of the base classifiers is achieved by training single learning algorithm on different data sets (bootstrap resampling) [11] [14] [15] [16], different parameters [14], or different features [1] [7] [8]. However, some works achieve diversity by training different learning algorithms on the same data.…”

Section: Related Workmentioning

confidence: 99%

“…Building ensembles of classifiers is a powerful method for obtaining better classification accuracy through combining the classification of multiple classifiers [1]. Boosting [2] [3] and bagging [2] [3] are the two most commonly used methods for building ensembles of homogenous classifiers.…”

Section: Introductionmentioning

confidence: 99%