中圖分類號： TP391 文獻標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.201173 中文引用格式： 謝斌,，林珊玲，林志賢,，等. 基于強化學(xué)習(xí)的特征工程算法研究[J].電子技術(shù)應(yīng)用,，2021，47(7)：29-32,，43. 英文引用格式： Xie Bin,，Lin Shanling，Lin Zhixian,，et al. Research on feature engineering algorithm based on reinforcement learning[J]. Application of Electronic Technique,，2021，47(7)：29-32,，43.
Research on feature engineering algorithm based on reinforcement learning
1.School of Physics and Information Engineering，F(xiàn)uzhou University,，F(xiàn)uzhou 350116,，China； 2.China Fujian Optoelectronic Information Science and Technology Innovation Laboratory,，F(xiàn)uzhou 350116,，China,； 3.School of Advanced Manufacturing, Fuzhou University，Quanzhou 362200,，China
Abstract： Feature engineering can automatically process and generate those highly discriminative features without human operation. Feature engineering is an inevitable and crucial part of machine learning. The article proposes a method based on reinforcement learning(RL), taking feature engineering as a Markov decision process(MDP), and proposes an approximate method based on the upper limit confidence interval algorithm(UCT) to solve the feature engineering of binary numerical data problem to automatically obtain the best transformation strategy. The effectiveness of the proposed method is verified on five public data sets. The FScore of the five public data sets is improved by an average of 9.032%. It is also compared with other papers that use finite element transformation for feature engineering. This method can indeed obtain highly discriminative features, improve the learning ability of the model, and obtain higher accuracy.
Key words : feature engineering,；reinforcement learning；machine learning
