3 citations · 6 across the 4 of their papers we have counts for
5 papers
Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks
Anita Eisenbürger, Daniel Otten, Anselm Hudde +1
Label noise, which refers to the mislabeling of instances in a dataset, can significantly impair classifier performance, increase model complexity, and affect feature selection. Wh…
An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features
Mengyisong Zhao, Morgan Harvey, David Cameron +2
Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is cl…
SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"
Zhixue Zhao, Ziqi Zhang, Frank Hopfgartner
Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias i…
Evaluation-as-a-Service: Overview and Outlook
Allan Hanbury, Henning Müller, Krisztian Balog +11
Evaluation in empirical computer science is essential to show progress and assess technologies developed. Several research domains such as information retrieval have long relied on…
Collaborative Search Trails for Video Search
Frank Hopfgartner, David Vallet, Martin Halvey +1
In this paper we present an approach for supporting users in the difficult task of searching for video. We use collaborative feedback mined from the interactions of earlier users o…