We have analyzed the theoretical complexity of several datasets, trying to relate this to the results achieved by four widely used classifiers. Making use of several data complexity measures, we studied in detail the problematics present in 21 microarray binary and multiclass datasets. Our main conclusions are the following:

Additionally, all the classifiers, except for PHCA, ranked last in terms of accuracy and F1 scores in at least one of the four classification problems. In conclusion, the validation results show that PHCA can perform well, or even better, than some of the widely used machine learning classifiers in solving classification problems.

Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used …

This study used a dataset of U.S. listed firms and a set of five relevant bankruptcy predictor variables covering the 2010–2018 period to explore the effect of six widely used synthetic balancing techniques and their effect on the performance of 11 machine-learning classifiers.

The proposed algorithm evolves from the Naïve Bayes classifier, which has been verified as one of the most useful tools for various classification tasks, such as document classification (Farid et ...

This is one of the most commonly used classification algorithms. The idea is to perform simple correlations between inputs and outputs. This helps explain how a …

Random Forest is a widely used classification and regression algorithm. As classification and regression are the most significant aspects of machine learning, we can say that the Random Forest Algorithm is one of the most important algorithms in machine learning. ... Land Use: Random Forest Classifier is also used to classify places with ...

K-Fold Cross-Validation is a widely used technique for evaluating classifiers in supervised learning. In this method, the dataset is divided into K equally sized subsets or folds. The classifier is trained on K-1 folds and tested on the remaining fold. This process is repeated K times, each time using a different fold for testing.

The classifiers used for satellite image classification are divided into two types: statistical and machine learning techniques, the performance of which depends on the data distribution. ... The maximum …

As one of the most commonly-used TSK fuzzy classifiers, the effectiveness of a zero-order Gaussian TSK fuzzy classifier has been widely demonstrated in [3], [19], [27], [36], [37] owing to its simplicity and universal approximation property. In a zero-order Gaussian TSK fuzzy classifier, each fuzzy rule takes Gaussian membership functions to ...

The most widely used classification methods are as follows- Minimum distance classifier (Bag & al, 2011), Euclidian distance (Mahoor et al., 2006), …

This article will learn a new Rule Based Data Mining classifier for classifying data and predicting class labels. This mining technique is widely used in various real-world business applications in machine learning. A rule-based classifier helps classify data and predict the possible outcome when rules scenarios are adequately defined.

Magnetic resonance imaging (MRI) is widely used in neuroradiology to detect brain lesions including stroke, vascular disease, and tumor tissue. ... Like the cross-dataset validation for classifier training, five-fold cross-site validation was used to assess classifier accuracy (see Fig.1C for an illustration). By ensuring that the data from ...

For example, Bagging is widely used in classification and regression. Besides, Random Forest (a variant of bagging), Random Subspace, AdaBoost (a sequential ensemble method) have been widely ...

in data mining [82]. Consequently, KNN has been studied over the past few decades and widely applied in many elds [8]. Thus, KNN comprises the baseline classi er in many pattern classi cation problems such as pattern recognition [84], text categorization [54], ranking models [83], object recognition [6], and event recognition [85] applications.

It is used widely, when the input variable is continuous and independent then the parameters are estimated by the Bayes rule, so that the probability of output variable is exactly predicted. If E 1, E 2,..., E n . are the selected genes from any sample of H, Naïve Bayes classifier classified the samples by using below formula with Bayes ...

The most widely-used measure is the area under the curve (AUC). As you can see from Figure 2, the AUC for a classifier with no power, essentially random guessing, is 0.5, because the curve follows …

Introduction. In machine learning, classification refers to predicting the label of an observation. In this tutorial, we'll discuss how to measure the success of a classifier for both binary and multiclass classification problems. We'll cover some of the most widely used classification measures; namely, accuracy, precision, recall, F-1 ...

Another widely used technique for model reuse, complementary to feature extraction, is fine-tuning Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in this case, the fully connected classifier) and these top layers.

Here, the Perceptron algorithm looks to minimize the objective function in order to predict the correct label for the data set. The objective function (L) and constraints are defined as follows ...

These are important for many applications […] where classifiers are used to select the best n instances of a set of data or when good class separation is crucial. — An Experimental Comparison Of Performance Measures For Classification, 2008. These metrics require that a classifier predicts a score or a probability of class membership.

First, to classify diabetes into predefined categories, we have employed three widely used classifiers, i.e., random forest, multilayer perceptron, and logistic regression. Second, for the predictive analysis of diabetes, long short-term memory (LSTM), moving averages (MA), and linear regression (LR) are used. To demonstrate the effectiveness ...

Random Forest is a widely used classification and regression algorithm. As classification and regression are the most significant aspects of machine learning, we can say that the …

Convolutional Neural Network (CNN) is the often used classifier for image processing, and it has shown accurate image filtering and grouping [4]. CNN is one of the artificial neural networks which is widely used for image identification. The Image processing methods are used for disease detection in leaves [5].

Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. ... and therefore, they are widely used in machine learning classification. The Bayes theorem is given in Eq. (16). (16) P A B = P ...

Earth observations (EO) image classification is one of the most widely used analysis techniques in the remote sensing (RS) community. Image classification techniques are used to automatically and analytically interpret a significant amount of data from various EO sensors for diverse applications, such as change detection, crop mapping, forest …

Among the most widely used classifiers in the wood identification studies are k-nearest neighbors (k−NN), support vector machine (SVM), and artificial neural network (ANN) (Hwang and Sugiyama, 2021). An image processing based wood species recognition approach is proposed in (Tou et al., 2009) where feature images are generated using …

Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined.

Some widely used classifiers include Softmax, Support Vector Machine (SVM), Random Forest, and Logistic Regression. In the review, it has been found that softmax is the most widely used classifier ...

open access Abstract A few of the popular data-mining techniques are clustering, classification, and association. The classification process simplifies the …