A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction

2026-06-04 · June-Woo Kim, Kangwook Jang, Minu Kim, Hyunju Lee arxiv

Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the NeckVibe Challenge dataset to distinguish phonotraumatic (PVH) and non-phonotraumatic (NPVH) from healthy controls. We propose a hierarchical feature engineering framework comprising: (i) static, (ii) dynamic, (iii) ratio-based, (iv) coupling features capturing source filter interactions. While univariate statistical analysis shows strong separability for PVH but limited significance for NPVH, our machine learning pipeline, tailored for high-dimensional feature integration, identifies that coupling features are crucial for both tasks. We achieve an AUC of 0.891 for PVH and 0.728 for NPVH, suggesting that while PVH is near-linearly separable, NPVH discrimination benefits from modeling non-linear feature interactions.

📄 PDF Abstract BibTeX arXiv:2606.07673

Code (0)

등록된 구현이 없습니다.

Tasks

Feature Engineering

Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning

2026-02-18 · Arun Vignesh Malarkkan, Wangyang Ying, Yanjie Fu arxiv

Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistical heuristics, yielding brittle features…

Multi-agent Reinforcement Learning Feature Engineering

Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

2018-06-01 · WS 2018 6 · Julia Ive, George Gkotsis, Rina Dutta, Robert Stewart 외

Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such anal…

Classification Decision Making Feature Engineering General Classification+2

Catch: Collaborative Feature Set Search for Automated Feature Engineering

2023-04-30 · journal 2023 4 · Guoshan Lu, Haobo Wang, Saisai Yang, Jing Yuan 외

Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it ha…

Automated Feature Engineering Decision Making Feature Engineering reinforcement-learning+1

PatchNet: A Tool for Deep Patch Classification

2019-02-16 · Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian 외

This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure tha…

Classification Deep Learning General Classification

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

2025-03-18 · Nikhil Abhyankar, Parshin Shojaee, Chandan K. Reddy

Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined tra…

Automated Feature Engineering Feature Engineering feature selection