Feature Multi-Selection among Subjective Features

TL;DR

This paper introduces theoretically-motivated algorithms for feature multi-selection that determine which features to judge and how many times, improving linear regression predictions in crowdsourced tasks involving subjective features.

Contribution

It presents novel algorithms for feature multi-selection that optimize judgment allocation among features, addressing subjective and noisy data in predictive modeling.

Findings

Effective in crowdsourced height and weight prediction tasks

Improves feature selection for subjective and culturally sensitive features

Demonstrates theoretical and empirical advantages over traditional methods

Abstract

When dealing with subjective, noisy, or otherwise nebulous features, the "wisdom of crowds" suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoretically-motivated `feature multi-selection' algorithms that choose, among a large set of candidate features, not only which features to judge but how many times to judge each one. We demonstrate the effectiveness of this approach for linear regression on a crowdsourced learning task of predicting people's height and weight from photos, using features such as 'gender' and 'estimated weight' as well as culturally fraught ones such as 'attractive'.