A New Classification Model Using a Decision Tree Generated from Hyperplanes in Dimensional Space

Abstract

One of the issues addressed by machine learning, with applications in various disciplines or fields such as the health sector and the agricultural sector among others, involves data classification. For this purpose, various models within supervised learning have been proposed and developed that allow for the classification of these data. However, one of the implications of the No-Free-Lunch theorems is that there is no optimal general-purpose model, i.e. there is no classifier model that achieves the best results for all problems presented. Hence the importance of proposing and implementing new classifier models, evaluating their performance, and comparing them with other classifier models in order to achieve models with good results in specific problems. This work presents a new classifier model that, by constructing hyperplanes from the training set, generates a decision tree and partitions the dimensional space. The proposed model was applied to different problems such as the [Formula: see text] logical function problem, where the proposed model managed to solve the problem, it was also applied to the Iris Dataset where one of the trees generated by the model managed to classify with 100% accuracy the test set, and finally, the proposed model was applied to the Pima Indians Diabetes Database and compared with other models using the accuracy value. The proposed model obtained an accuracy of 81.81%, achieving the best result in the same way as the Random Forest Classifier. The results obtained in this work show that the proposed model manages to partition the dimensional space adequately from the training set and thus competitively classify the data with other state-of-the-art models.