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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. [ABSTRACT FROM AUTHOR]

Age-Related Macular Degeneration (AMD) is a dangerous, chronic, and progressive illness that mostly affects people over 60 years old. This disease is related to the appearance of drusen: deposits of extracellular material located in the macular region. One way to effectively and non-invasively pre-diagnose AMD is by detecting the presence of drusen in fundus images. In this work we propose a new method that combines Digital Image Processing, Mathematical Morphology and a robust and powerful Machine Learning model: a Support Vector Machine (SVM). The enclosed macular region is subjected to a contrast enhancement method, followed by the application of basic morphological operations. We use invariant moments as the features of the processed image. The resulting vector is classified by an SVM as positive or negative for drusen. The proposed method is able to discriminate between healthy and afflicted cases with a classification accuracy that outperforms many well-regarded state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k -NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles. [ABSTRACT FROM AUTHOR]

The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of executing the pattern classification task, but its performance is not competitive when compared to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a simple mathematical transform, whose application eliminates the subtractive alterations between patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support vector machines, and neural networks). By choosing balanced accuracy as a performance measure, our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes the new model competitive against the best classifiers, and sometimes beats them. After applying the Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal competes successfully with the most effective classifiers of the state of the art. [ABSTRACT FROM AUTHOR]