As we explained in a previous paper comparing classifiers (Fernández-Delgado, Cernadas, Barro, & Amorim, 2014), provided that the size of the model collection used in the current comparison is large enough, we can assume that the best performance, measured in terms of squared correlation (R2...
We compare the classification performance of FMM-LASSO with that of support vector machine (SVM), Random Forest, K-nearest Neighbor (KNN), Nave Bayes and Logistic Regression classifiers, with and without LASSO. The results prove that FMM-LASSO performs better as compared to other approaches.Singh...
For example, using a small scale for \(\sqrt{{\omega }_{1}}\) such as e−5, the R function bayesglm in R package ARM (which implements penalized logistic regression with Cauchy priors) will converge to a mode where almost all coefficients are shrunken to very small values, even ...
the different dimensionalityreduction algorithmsand the selection of classifiers, a drug-target interactions prediction model is established. On the four datasets enzymes, ion channels,GPCRs, andnuclear receptors, satisfactory overall prediction accuracy are obtained. According to the comparison with other e...
We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying...
In Section 4.2, these classifiers were utilized to validate the discriminative power of the learned features, where KNN exhibited a superior classification performance (refer to Table 1). As a distance-based and non-parametric approach, KNN offers several advantages, including simplicity and intuition...