Combining Boosting and Active Learning for Mining Multi-Class Genomic Data
|Title||Combining Boosting and Active Learning for Mining Multi-Class Genomic Data|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Tertiary Authors||Manderick, B|
|Conference Name||25th Belgian-Dutch Conference on Machine Learning (Benelearn)|
|Conference Location||Kortrijk, Belgium|
Boosting and active learning achieve high classification rate in many real world machine learning for data mining applications. This paper presents an optimal ensemble learning to improve the prediction accuracy of multi-class DNA variant classification employing boosting and active learning. We use naive Bayes classifier and clustering to find the most informative unlabeled DNA variants as part of active learning and use boosting as a base classifier. The strategy of combining boosting and active learning is evaluated based on genomic data (148 Exome data sets) of Brugada syndrome from the Centre of Medical Genetics, VUB UZ Brussel.