This paper proposes Iterative Similarity Bagging (ISB), assisted by Bayesian Ridge Regression (BRR). BRR serves as a domain-oriented supervised feature selection method, choosing essential features by calculatin
The set of decision trees (or continuous y regression trees) is constructed by performing bootstrapping on the data sets and averaging or acquiring pattern prediction (called “bagging”) from the trees. Subsampling of features is used to reduce generalization errors [32]. An ancillary result of...
Bagging (bootstrap aggregation): the algorithm trains different models with different subsets of data in parallel, and aggregates the outputs by majority voting (classification) or averaging (regression) The use of machine learning algorithms in research changes the paradigms of traditional research (Fi...
In the Bayesian approach to inference, all unknown quantities contained in a probability model for the observed data are treated as random variables. This is in contrast to the frequentist view described in Chap. 2 in which parameters are treated as fixe
Random forests are a set of decision trees or regression trees that work together [31]. The set of decision trees (or continuous y regression trees) is constructed by performing bootstrapping on the data sets and averaging or acquiring pattern prediction (called “bagging”) from the trees. ...
Elastic Net is a regression model that combines the Lasso (L1) and Ridge (L2) regularization methods. While Lasso encourages feature selection, Ridge provides greater regularization (shrinkage). Elastic Net combines the benefits of both methods, improving performance with high-dimensional datasets, and...
Elastic Net is a regression model that combines the Lasso (L1) and Ridge (L2) regularization methods. While Lasso encourages feature selection, Ridge provides greater regularization (shrinkage). Elastic Net combines the benefits of both methods, improving performance with high-dimensional datasets, and...