A Human-Centered Approach for Improving Supervised Learning

TL;DR

This paper introduces a human-centered, behavior-inspired ensemble learning algorithm that enhances supervised learning by balancing performance, resource efficiency, and explainability, demonstrated through experiments on real-world datasets.

Contribution

It proposes a novel human-centered, behavior-inspired algorithm that improves ensemble learning efficiency and explainability, addressing practical challenges in real-world supervised learning applications.

Findings

Outperforms existing ensemble methods in accuracy.

Reduces time and resource consumption.

Enhances explainability of ensemble predictions.

Abstract

Supervised Learning is a way of developing Artificial Intelligence systems in which a computer algorithm is trained on labeled data inputs. Effectiveness of a Supervised Learning algorithm is determined by its performance on a given dataset for a particular problem. In case of Supervised Learning problems, Stacking Ensembles usually perform better than individual classifiers due to their generalization ability. Stacking Ensembles combine predictions from multiple Machine Learning algorithms to make final predictions. Inspite of Stacking Ensembles superior performance, the overhead of Stacking Ensembles such as high cost, resources, time, and lack of explainability create challenges in real-life applications. This paper shows how we can strike a balance between performance, time, and resource constraints. Another goal of this research is to make Ensembles more explainable and intelligible using the Human-Centered approach. To achieve the aforementioned goals, we proposed a Human-Centered Behavior-inspired algorithm that streamlines the Ensemble Learning process while also reducing time, cost, and resource overhead, resulting in the superior performance of Supervised Learning in real-world applications. To demonstrate the effectiveness of our method, we perform our experiments on nine real-world datasets. Experimental results reveal that the proposed method satisfies our goals and outperforms the existing methods.