Polar Encoding: A Simple Baseline Approach for Classification with Missing Values

• URL: http://arxiv.org/abs/2210.01905v4
• Date: Wed, 15 May 2024 11:40:20 GMT
• Title: Polar Encoding: A Simple Baseline Approach for Classification with Missing Values
• Authors: Oliver Urs Lenz, Daniel Peralta, Chris Cornelis,
• Abstract summary: polar encoding is a representation of $[0,1]$-valued attributes with missing values. It does not require imputation, ensures that missing values are equidistant from non-missing values, and lets decision tree algorithms choose how to split missing values. We show that, in terms of the resulting classification performance, polar encoding performs better than the state-of-the-art strategies "multiple imputation by chained equations" and "multiple imputation with denoising autoencoders"
• Score: 1.7205106391379026
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose polar encoding, a representation of categorical and numerical $[0,1]$-valued attributes with missing values to be used in a classification context. We argue that this is a good baseline approach, because it can be used with any classification algorithm, preserves missingness information, is very simple to apply and offers good performance. In particular, unlike the existing missing-indicator approach, it does not require imputation, ensures that missing values are equidistant from non-missing values, and lets decision tree algorithms choose how to split missing values, thereby providing a practical realisation of the "missingness incorporated in attributes" (MIA) proposal. Furthermore, we show that categorical and $[0,1]$-valued attributes can be viewed as special cases of a single attribute type, corresponding to the classical concept of barycentric coordinates, and that this offers a natural interpretation of polar encoding as a fuzzified form of one-hot encoding. With an experiment based on twenty real-life datasets with missing values, we show that, in terms of the resulting classification performance, polar encoding performs better than the state-of-the-art strategies "multiple imputation by chained equations" (MICE) and "multiple imputation with denoising autoencoders" (MIDAS) and -- depending on the classifier -- about as well or better than mean/mode imputation with missing-indicators.

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