2020/11/14 by Mohammad Taha Toghani, Genevera I. Allen · 6 citations
Computer Science · Mathematics · #Adaptive sampling #Algorithms and Data Compression #Artificial intelligence #Boosting (machine learning) #Computer science #Feature (linguistics) #Machine Learning and Algorithms #Machine Learning and Data Classification #Mathematics #Pattern recognition (psychology) #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.1109/bigcomp51126.2021.00023
arxiv created 2020/11/14 · openalex publication_date 2021/01/01 · arxiv updated 2021/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Boosting methods are among the best generalpurpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is more interpretable. We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration. By sequentially learning on tiny subsets of the data, our approach is computationally faster than classic boosting algorithms. MP-Boost upweights important features and challenging instances, hence adaptively selects the most relevant minipatches for learning. The learned probability distributions aid in interpretation of our method. We empirically demonstrate the interpretability and comparative accuracy of our algorithm on a variety of binary classification tasks.