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99.455  Articles
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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

There is an urgent need to reduce customer complaints because they damage reputations and incur losses. This study predicts the likelihood of complaint about a new production order using its intrinsic features. Customer complaints, however, are relatively... 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 work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence ... see more

The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new me... see more

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

Topik penelitian ini di bidang EDM (Educational Datamining) yang bertujuanmemanfaatkan datamining dalam memperoleh informasi yang lebih bernilai daridatabase akademik Perguruan Tinggi. Penggunaan data pendaftaran mahasiswabaru karena karakteristik datanya... see more

This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support... 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

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