Comparing Machine Learning-Centered Approaches for Forecasting Language Patterns During Frustration in Early Childhood
This work addresses a critical gap in understanding children's self-regulation through language, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of forecasting language patterns during frustration in early childhood by comparing machine learning methods, finding that decision tree-based algorithms outperform regression and neural networks on high-dimensional, irregular data.
When faced with self-regulation challenges, children have been known the use their language to inhibit their emotions and behaviors. Yet, to date, there has been a critical lack of evidence regarding what patterns in their speech children use during these moments of frustration. In this paper, eXtreme Gradient Boosting, Random Forest, Long Short-Term Memory Recurrent Neural Networks, and Elastic Net Regression, have all been used to forecast these language patterns in children. Based on the results of a comparative analysis between these methods, the study reveals that when dealing with high-dimensional and dense data, with very irregular and abnormal distributions, as is the case with self-regulation patterns in children, decision tree-based algorithms are able to outperform traditional regression and neural network methods in their shortcomings.