Ensemble cluster pruning via convex‐concave programming

Abstract

Summary Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.

Gradient boosting DD‐MLP Net: An ensemble learning model using near‐infrared spectroscopy to classify after‐stroke dyskinesia degree during exercise

“…Ensemble learning helps to overcome problems in a single classifier system: (1) the calculation problem: underearning for a weak classifier in the learning process, (2) the statistical problem: inability to capture the whole hypothesis space when the learning data set is too small, (3) the characterization problem: incapacity to the find the real objective function from the hypothesis space [7,8]. Ensemble learning can aggregate the decisions of different learners, and its performance depends on the accuracy of each learner and the diversity degree of learners [9]. Ensemble learning is a low-cost and efficient model.…”