Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to
increase the interpretability and explainability of black box Machine Learning (ML) …

We review recent advances in modal regression studies using kernel density estimation.
Modal regression is an alternative approach for investigating the relationship between a …

In this article, we propose a novel composite learning framework for interval type-2 (IT2)
fuzzy logic systems (FLSs) to train regression models with a high accuracy performance and …

A novel neural network, namely, broad learning system (BLS), has shown impressive
performance on various regression and classification tasks. Nevertheless, most BLS models …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

In the long-term use of the Case-based reasoning (CBR) model for early-stage construction
cost estimation, a typical issue is the unstable knowledge structure when the actual data …

Most existing feature selection methods in literature are linear models, so that the nonlinear
relations between features and response variables are not considered. Meanwhile, in these …

Additive models have attracted much attention for high-dimensional regression estimation
and variable selection. However, the existing models are usually limited to the single-task …

Additive models have been burgeoning in data analysis due to their flexible representation
and desirable interpretability. However, most existing approaches are constructed under …

Manifold learning-based face hallucination technologies have been widely developed
during the past decades. However, the conventional learning methods always become …