Research on C5.0 Algorithm Improvement and the Test in Lightning Disaster Statistics

Hou, Shuonan; R, Hou; Shi, Xinming; Wang, Jun; Yuan, Chengshang

doi:10.14257/ijca.2014.7.1.15

Cited by 7 publications

(1 citation statement)

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“…To overcome the limitation of undersampling, a K-means clustering-based undersampling method is employed to select the samples near the boundary since the border samples are the most informative ones and play an important role in the classification [17–19], thereby preserving the maximum of useful samples. Meanwhile, we can adjust the strength to reconstruct small size of subset for training by boosted C5.0, which has been considered as the most effective algorithms for breast cancer diagnosis [14, 15, 20]. The objective of this paper is to discuss the clustering-based undersampling method for training boosted C5.0 for class imbalanced data, especially breast cancer prediction data, and we will mainly focus on the undersampling strategy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Breast Cancer from Imbalance Respect Using Cluster-Based Undersampling Method

Zhang

Chen

Abid

2019

Journal of Healthcare Engineering

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To overcome the two-class imbalanced problem existing in the diagnosis of breast cancer, a hybrid of K-means and Boosted C5.0 (K-Boosted C5.0) is proposed which is based on undersampling. K-means is utilized to select the informative samples near the boundary. During the training phase, the K-means algorithm clusters the majority and minority instances and selects a similar number of instances from each cluster. Boosted C5.0 is then used as the classifier. As there is one different instance selection factor via clustering that encourages the diversity of the training subspace in K-Boosted C5.0, it would be a great advantage to get better performance. To test the performance of the new hybrid classifier, it is implemented on 12 small-scale and 2 large-scale datasets, which are the often used datasets in class imbalanced learning. The extensive experimental results show that our proposed hybrid method outperforms most of the competitive algorithms in terms of Matthews' correlation coefficient (MCC) and accuracy indices. It can be a good alternative to the well-known machine learning methods.

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