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Stochastic schemata exploiter-based optimization of hyper-parameters for XGBoost

Makino Hiroya, Kita Eisuke

Computer Assisted Methods in Engineering and Science

2024

Vol. 31, no. 1

113--132

XGBoost is well-known as an open-source software library that provides a regularizing gradient boosting framework. Although it is widely used in the machine learning field, its performance depends on the determination of hyper-parameters. This study focuses on the optimization algorithm for hyper-parameters of XGBoost by using Stochastic Schemata Exploiter (SSE). SSE, which is one of Evolutionary Algorithms, is successfully applied to combinatorial optimization problems. SSE is applied for optimizing hyper-parameters of XGBoost in this study. The original SSE algorithm is modified for hyper-parameter optimization. When comparing SSE with a simple Genetic Algorithm, there are two interesting features: quick convergence and a small number of control parameters. The proposed algorithm is compared with other hyper-parameter optimization algorithms such as Gradient Boosted Regression Trees (GBRT), Tree-structured Parzen Estimator (TPE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Random Search in order to confirm its validity. The numerical results show that SSE has a good convergence property, even with fewer control parameters than other methods.

Greedy incremental support vector regression

Ruta Dymitr, Cen Ling, Vu Quang Hieu

Annals of Computer Science and Information Systems

2019

Vol. 18

7--9

Support Vector Regression (SVR) is a powerful supervised machine learning model especially well suited to the normalized or binarized data. However, its quadratic complexity in the number of training examples eliminates it from training on large datasets, especially high dimensional with frequent retraining requirement. We propose a simple two-stage greedy selection of training data for SVR to maximize its validation set accuracy at the minimum number of training examples and illustrate the performance of such strategy in the context of Clash Royale Challenge 2019, concerned with efficient decks' win rate prediction. Hundreds of thousands of labelled data examples were reduced to hundreds, optimized SVR was trained on to maximize the validation R2 score. The proposed model scored the first place in the Cash Royale 2019 challenge, outperforming over hundred of competitive teams from around the world.