A New Structure for Perceptron in Categorical Data Classification

Artificial neural networks are among the most significant models in machine learning that use numeric inputs. This study presents a new single-layer perceptron model based on categorical inputs. In the proposed model, every quality value in the training dataset receives a trainable weight. Input data is classified by determining the weight vector that corresponds to the categorical values in it. To evaluate the performance of the proposed algorithm, we have used ۱۰ datasets. We have compared the performance of the proposed method to that of other machine learning models, including neural networks, support vector machines, naïve Bayes classifiers, and random forests. According to the results, the proposed model resulted in a ۳۶% reduction in memory usage when compared to baseline models across all datasets. Moreover, it demonstrated a training speed enhancement of ۵۴.۵% for datasets that contained more than ۱۰۰۰ samples. The accuracy of the proposed model is also comparable to other machine learning models.

کلیدواژه ها:

Neural network ، qualitative data ، categorical data ، non-numeric data ، binary classification