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Extending the Minimal Learning Machine for Classification and Regression on Interval-Valued Data
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DOI：10.1007/978-3-032-15987-8_37.png)
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Abstract
En 中文
Solving machine learning problems involving interval-valued data is a challenging task that arises in various real-world applications, such as heart rate prediction and astronomical data analysis. Motivated by this, several nonlinear regression methods and classifiers have been proposed in recent years to address this specific data type. In this paper, we introduce two novel variants of the Minimal Learning Machine (MLM) adapted to interval-valued inputs, targeting both regression and classification tasks. The proposed variants explicitly model a direct dependency between input and output intervals: the lower (upper) bound of the output is predicted based on both bounds of the input. For regression tasks, we compare our models against seven state-of-the-art nonlinear approaches: three Extreme Learning Machine (ELM) variants for interval-valued data, two kernel regression extensions, without explicitly modeling interval dependencies. For classification, we evaluate three interval-based logistic regression models. Experimental results on synthetic datasets with varying configurations and on real-world data demonstrate that our MLM variants achieve comparable or superior performance. These findings highlight the effectiveness of the proposed methods and their potential as competitive alternatives for learning from interval-valued data.
Keywords:
Minimal Learning Machine
Interval-Valued Data
Nonlinear Regression
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