Book classification is very popular in digital libraries. Book rating prediction is crucial to improve the care of readers. The commonly used techniques are decision tree, Naïve Bayes (NB), neural networks, etc. Moreover, mining book data depends on feature selection, data pre-processing, and data preparation. This paper proposes the solutions of knowledge representation optimization as well as feature selection to enhance book classification and point out appropriate classification algorithms. Several experiments have been conducted and it has been found that NB could provide best prediction results. The accuracy and performance of NB can be improved and outperform other classification algorithms by applying appropriate strategies of feature selections, data type selection as well as data transformation.

中文翻译：

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使用朴素贝叶斯训练大型数据集的分类优化

图书分类在数字图书馆中非常流行。图书评分预测对于提高读者的阅读能力至关重要。常用的技术有决策树，朴素贝叶斯（NaïveBayes，NB），神经网络等。此外，挖掘书籍数据还取决于特征选择，数据预处理和数据准备。本文提出了知识表示优化和特征选择的解决方案，以增强图书分类并指出合适的分类算法。已经进行了一些实验，并且发现NB可以提供最佳的预测结果。通过应用适当的特征选择，数据类型选择和数据转换策略，可以提高NB的准确性和性能，并胜过其他分类算法。