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245.952  Articles
1 of 24.596 pages  |  10  records  |  more records»
Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recal... see more

Imbalanced dataset merupakan hal yang sering ditemukan secara alami dalam proses penambangan data. Kondisi ini sangat mempengaruhi keakuratan klasifikasi data seperti yang terjadi dalam klasifikasi komentar program Kampus Merdeka yang peneliti lakukan. Pe... see more

One of the problems that are often faced by classifier algorithms is related to the problem of imbalanced data. One of the recommended improvement methods at the data level is to balance the number of data in different classes by enlarging the sample to t... see more

The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with ... see more

The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with ... see more

Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algo... see more

Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algo... see more

Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This... see more

This paper proposes a Modified Balanced Random Forest (MBRF) algorithm as a classification technique to address imbalanced data. The MBRF process changes the process in a Balanced Random Forest by applying an under-sampling strategy based on clustering te... see more

1 of 24.596 pages  |  10  records  |  more records»