# MiSC: Mixed Strategies Crowdsourcing

## TL;DR

MiSC is a versatile framework that combines traditional crowdsourcing quality evaluation with tensor completion methods, improving label aggregation accuracy through an iterative Tucker algorithm.

## Contribution

This work introduces MiSC, a novel framework integrating conventional crowdsourcing and tensor completion, along with a new iterative Tucker label aggregation algorithm.

## Key findings

- Outperforms state-of-the-art label aggregation methods
- Demonstrates effectiveness through extensive experiments
- Successfully integrates mixed strategies for crowdsourcing

## Abstract

Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose $\textit{MiSC}$ ($\textbf{Mi}$xed $\textbf{S}$trategies $\textbf{C}$rowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments.