Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques
نویسندگان
چکیده
منابع مشابه
ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network
every woman is at risk of ovarian cancer; about 90 percent of women who develop ovarian cancer are above 40 years of age, with the high number of ovarian cancers occurring at the age of 60 years and above. early and correct diagnosis of ovarian cancer can allow proper treatment and as a result reduce the mortality rate. in this paper, we proposed a hybrid of synthetic minority over-sampling tec...
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of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques
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An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (i...
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Abstract—Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalance...
متن کاملBird Classification using Ensemble Classifiers
This working note summarizes our submission to the LifeCLEF 2014 Bird Task which combines the outputs from a Python and Matlab classification system. The features used for both systems include Mel-Frequency Cepstral Coefficients (MFCC), time-averaged spectrograms and the provided meta-data. The Python subsystem combines a large ensemble of different classifiers with different subsets of the fea...
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ژورنال
عنوان ژورنال: Bayero Journal of Pure and Applied Sciences
سال: 2019
ISSN: 2006-6996,2006-6996
DOI: 10.4314/bajopas.v11i2.17