Minimal Learning Machine for Multi-Label Learning

• URL: http://arxiv.org/abs/2305.05518v1
• Date: Tue, 9 May 2023 15:16:50 GMT
• Title: Minimal Learning Machine for Multi-Label Learning
• Authors: Joonas H\"am\"al\"ainen, Amauri Souza, C\'esar L. C. Mattos, Jo\~ao P. P. Gomes, Tommi K\"arkk\"ainen
• Abstract summary: Distance-based supervised method, the minimal learning machine, constructs a predictive model from data. In this paper, we evaluate how this technique and its core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose methods and evaluate how this technique and its core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting. Although the proposal is one of the simplest methods in the multi-label learning literature, it achieves state-of-the-art performance for small to moderate-sized multi-label learning problems. Besides its simplicity, the proposed method is fully deterministic and its hyper-parameter can be selected via ranking loss-based statistic which has a closed form, thus avoiding conventional cross-validation-based hyper-parameter tuning. In addition, due to its simple linear distance mapping-based construction, we demonstrate that the proposed method can assess predictions' uncertainty for multi-label classification, which is a valuable capability for data-centric machine learning pipelines.

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