Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing complete classification algorithm is accurate efficient while also considering costs. proposed method integrates Bayesian handle incremental data. approach generates suitable dynamically based objects cost constraints. To data, are integrated, kernel functions obtained from estimation added linear support vector machine construct classifier using directed acyclic networks nodes (QKE). experimental findings demonstrate effectiveness adaptability suggested technique. In terms accuracy, speed, practical application impact, outperforms competition, with an accuracy difference conventional algorithms being less than 1%. With improved reduced expense as increases, for comparable previous algorithms. addresses critical issues need be resolved by methods, such inability process failure take categorization into account. By integrating QKE, achieves high maintaining performance when sequences Overall, theoretical technique, which offers promising solution handling tasks require efficiency.