Over time, a myriad of applications have been generated for pattern classification algorithms. Several case studies include parametric classifiers such as the Multi-Layer Perceptron (MLP) classifier, which is one of the most widely used today. Others use non-parametric classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Adaboost, and Random Forest (RF). However, there is still little work directed toward a new trend in Artificial Intelligence (AI), which is known as eXplainable Artificial Intelligence (X-AI). This new trend seeks to make Machine Learning (ML) algorithms increasingly simple and easy to understand for users. Therefore, following this new wave of knowledge, in this work, the authors develop a new pattern classification methodology, based on the implementation of the novel Minimalist Machine Learning (MML) paradigm and a higher relevance attribute selection algorithm, which we call dMeans. We examine and compare the performance of this methodology with MLP, NB, KNN, SVM, Adaboost, and RF classifiers to perform the task of classification of Computed Tomography (CT) brain images. These grayscale images have an area of 128 × 128 pixels, and there are two classes available in the dataset: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH), which were classified using the Leave-One-Out Cross-Validation method. Most of the models tested by Leave-One-Out Cross-Validation performed between 50% and 75% accuracy, while sensitivity and sensitivity ranged between 58% and 86%. The experiments performed using our methodology matched the best classifier observed with 86.50% accuracy, and they outperformed all state-of-the-art algorithms in specificity with 91.60%. This performance is achieved hand in hand with simple and practical methods, which go hand in hand with this trend of generating easily explainable algorithms.

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations.

This work introduces an innovative machine learning algorithm based on the minimalist machine learning paradigm, called matrix transformations bootstrap. Evaluated on 15 medical datasets, ranging from 3 to 1626 attributes, the methodology incorporates matrix transformations like rotation and shearing to improve dataset separation in binary classification tasks. Additionally, random feature selection is applied via the bootstrap method, resulting in two new attributes that can be visualized on the Cartesian plane while achieving substantial dimensionality reduction. The results show significant classification performance improvements over traditional algorithms like k-NN, SVM, Bayesian models, ensembles, neural networks, and logistic functions, evaluated using balanced accuracy, recall, and F1-score.

This study introduces a feature selection technique known as dMeans, focusing on 10 datasets with only two classes, with moderate dimensions ranging from 168 to 10,000 attributes. The technique calculates the relevance of each feature by considering the mean values between classes for individual features. The result of dMeans is a relevance permutation of the features, providing a nuanced understanding of feature importance based on their mean values across different classes. A significant positive impact on the performance of classifiers, including 1-NN, 3-NN, 5-NN, Naïve Bayes, Support Vector Machine, Random Forest, and Adaboost, has been observed. The application of the dMeans impacts in notable improvements in predictive efficiency for the classifiers, as indicated by optimized metrics such as F1-Score, Recall and Precision. Importantly, dMeans significantly reduces the number of features required to achieve classifier performance, often surpassing it. This study offers an effective strategy which provides valuable insights into the importance of feature selection in datasets with moderate dimensionality. dMeans enhance the performance of classification models while optimizing computational resources by reducing the number of features without compromising predictive quality.