A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors
This work presents a novel ensemble regression algorithm inspired by quantum mechanics, offering potential improvements in accuracy and robustness for machine learning practitioners, though it appears incremental as it builds upon existing methods like Random Forest.
The authors tackled the problem of improving ensemble regression by proposing a Quantum-Inspired Subspace Ensemble Method that uses quantum mechanics principles to enhance feature selection and diversity, resulting in Quantum-Inspired Forest regressors that demonstrate significant robustness with default hyperparameters on most datasets.
We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate Quantum-Inspired Subspace Method into Random Forest and propose Quantum-Inspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of Quantum-Inspired Forest and Random Forest in multiple hyperparameter settings. Quantum-Inspired Forest proves the significant robustness of the default hyperparameters on most data sets. The contribution of this work is two-fold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms.