This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. T...

Selecting appropriate features has become a key task when dealing with high-dimensional data. We present a new algorithm designed to find an optimal solution for classification tasks. Our approach combines forward selection, backward elimination and exhaustive search. We demonstrate its capabilities and limits using artificial and real world data sets. Regarding artificial data sets interleavin...

Traditionally, feature selection methods work directly on labeled examples. However, the availability of labeled examples cannot be taken for granted for many real world applications, such as medical diagnosis, forensic science, fraud detection, etc, where labeled examples are hard to find. This practical problem calls the need for “semi-supervised feature selection” to choose the optimal set o...

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a test...

in this paper, financial services in the iranian as a sector with a leontief production function, this sector buys inputs from other sectors for its productions and provides services to expedite the cash fellows and risk transformation to other sectors, the first relations are called backward linkage and the later relations are called forward linkage, for quantification of the linkage , employm...