PPFS: Predictive Permutation Feature Selection

TL;DR

PPFS is a new wrapper-based feature selection method leveraging Markov Blanket concepts, capable of handling both classification and regression tasks with categorical and continuous features, outperforming existing algorithms.

Contribution

Introducing PPFS, a universal wrapper feature selection method using a novel CI test based on the knockoff framework, applicable to diverse supervised learning tasks.

Findings

PPFS outperforms state-of-the-art Markov blanket algorithms.

PPFS surpasses well-known wrapper methods in empirical evaluations.

The method is applicable to both classification and regression datasets.

Abstract

We propose Predictive Permutation Feature Selection (PPFS), a novel wrapper-based feature selection method based on the concept of Markov Blanket (MB). Unlike previous MB methods, PPFS is a universal feature selection technique as it can work for both classification as well as regression tasks on datasets containing categorical and/or continuous features. We propose Predictive Permutation Independence (PPI), a new Conditional Independence (CI) test, which enables PPFS to be categorised as a wrapper feature selection method. This is in contrast to current filter based MB feature selection techniques that are unable to harness the advancements in supervised algorithms such as Gradient Boosting Machines (GBM). The PPI test is based on the knockoff framework and utilizes supervised algorithms to measure the association between an individual or a set of features and the target variable. We also propose a novel MB aggregation step that addresses the issue of sample inefficiency. Empirical evaluations and comparisons on a large number of datasets demonstrate that PPFS outperforms state-of-the-art Markov blanket discovery algorithms as well as, well-known wrapper methods. We also provide a sketch of the proof of correctness of our method. Implementation of this work is available at \url{https://github.com/atif-hassan/PyImpetus}