Evolving imputation strategies for missing data in classification problems with TPOT
This addresses the need for automated imputation selection in machine learning pipelines, reducing human intervention, though it is incremental as it builds on existing TPOT library and methods.
The paper tackles the problem of missing data in classification by using genetic programming to automatically search for effective combinations of imputation methods, feature pre-processing, and classifiers, showing that it can find increasingly better pipelines for various classification problems.
Missing data has a ubiquitous presence in real-life applications of machine learning techniques. Imputation methods are algorithms conceived for restoring missing values in the data, based on other entries in the database. The choice of the imputation method has an influence on the performance of the machine learning technique, e.g., it influences the accuracy of the classification algorithm applied to the data. Therefore, selecting and applying the right imputation method is important and usually requires a substantial amount of human intervention. In this paper we propose the use of genetic programming techniques to search for the right combination of imputation and classification algorithms. We build our work on the recently introduced Python-based TPOT library, and incorporate a heterogeneous set of imputation algorithms as part of the machine learning pipeline search. We show that genetic programming can automatically find increasingly better pipelines that include the most effective combinations of imputation methods, feature pre-processing, and classifiers for a variety of classification problems with missing data.