More Consideration for the Perceptron
This is an incremental improvement for machine learning practitioners seeking simple yet effective classifiers for non-linear tasks.
The paper tackles the problem of the perceptron's limited ability to handle non-linear data by introducing the gated perceptron, which incorporates an additional input as the product of existing inputs to capture non-linear interactions, resulting in more distinct decision regions and competitive performance with state-of-the-art classifiers on datasets like Iris, PIMA Indian, and Breast Cancer Wisconsin.
In this paper, we introduce the gated perceptron, an enhancement of the conventional perceptron, which incorporates an additional input computed as the product of the existing inputs. This allows the perceptron to capture non-linear interactions between features, significantly improving its ability to classify and regress on complex datasets. We explore its application in both linear and non-linear regression tasks using the Iris dataset, as well as binary and multi-class classification problems, including the PIMA Indian dataset and Breast Cancer Wisconsin dataset. Our results demonstrate that the gated perceptron can generate more distinct decision regions compared to traditional perceptrons, enhancing its classification capabilities, particularly in handling non-linear data. Performance comparisons show that the gated perceptron competes with state-of-the-art classifiers while maintaining a simple architecture.