Approximate Imputation Method for Missing Data in Machine Learning

An approximate imputation method called k-ANNO is proposed to handle the problems of missing data in machine learning field given a missing sample. The proposed method begins by constructing an offline graph to approximately search nearest neighbors of the partially missing sample efficiently. Then a fast quadratic programming algorithm is utilized to determine the optimal weight for each neighbor. Finally, unmissed parts of the neighbors are used to impute the missing attributes by the estimated weights. Users get the freedom to weigh up between efficiency and imputation accuracy. The widespread data missing problems are well solved in this paper and k-ANNO is able to depress the impact of missing data effectively. Experiments on various well known datasets show that when the speedup rate parameters are between 2 and 10, k-ANNO method outperforms existing ones such as mean imputation or C-Means imputation etc. and the classification error and the regression error are 1% to 4% and 0.5-2.0 lower than those, respectively. Meanwhile, k-ANNO outperforms naïve k-NN imputation with a faster efficiency increased by 35%-320% faster. © 2017, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.