This paper proposes a new framework, named Deja-Vu+, which is an extension of Deja Vu framework, a classic study on hyper-heuristic framework with 2R (Record and Recall) modules. Deja-Vu+ has the ability to handle two other domains, namely regression and unsupervised learning. The extension examines the strength of Deja-Vu+ for solving regression and unsupervised learning tasks. The regression problems are treated here as multiclass classification tasks, and unsupervised learning tasks are considered as clustering problems. The proposed framework is tested on a number of regression and unsupervised learning benchmark problems and has shown promising results to handle regression as classification. The framework attains an overall average accuracy of 70% for regression and clustering data sets. Deja-Vu+ is knowledge-rich hyper-heuristic framework, which is capable enough to transfer knowledge successfully. This knowledge transfer improves the performance of learning by avoiding the extensive heuristic search process. Our experimental results show that using previously attained knowledge to reduce the computational effort is beneficial in solving multi-disciplinary machine learning problems.

本文提出了一个名为Deja-Vu +的新框架，它是Deja Vu框架的扩展，它是对带有2R（记录和调用）模块的超启发式框架的经典研究。Deja-Vu +具有处理其他两个领域的能力，即回归和无监督学习。该扩展将检查Deja-Vu +在解决回归和无人监督学习任务方面的优势。回归问题在这里被视为多类分类任务，无监督学习任务被视为聚类问题。所提出的框架在许多回归和无监督学习基准问题上进行了测试，并显示出将回归作为分类进行处理的有希望的结果。该框架针对回归和聚类数据集的总体平均准确度达到70％。Deja-Vu +是知识丰富的超启发式框架，这足以成功地传递知识。这种知识转移避免了广泛的启发式搜索过程，从而提高了学习性能。我们的实验结果表明，使用先前获得的知识来减少计算量对于解决多学科的机器学习问题是有益的。