Proper selection of variables is necessary when dealing with large number of input dimensions in regression problems. In the paper, we investigate the behaviour of landscape that is formed when using Delta test as the optimization criterion. We show that simple and greedy Forward-backward selection procedure with multiple restarts gives optimal results for data sets with large number of samples...

This paper, I have focused on the tax side of the fiscal policy to  investigate the past and future behavior of fiscal sustainability in Iran. To do so, I have employed two different forward-looking and backward-looking approaches. First, the backward-looking approach is the fiscal policy rule proposed by Daving & Leeper (2011). Precisely, this rule determines that whether the fiscal policy is ...

We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed ...

Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classification results. So we put forward a feature selection approach, IIRCT, based on interclass and intraclass relative contributions of terms in the paper. In our proposed algorithm, t...

We derive formal selection rules for Stimulated Brillouin Scattering (SBS) in structured waveguides. Using a group-theoretical approach, we show how the waveguide symmetry determines which optical and acoustic modes interact for both forward and backward SBS. We present a general framework for determining this interaction and give important examples for SBS in waveguides with rectangular, trian...

In this chapter, we develop and evaluate a feature selection algorithm for Bayesian support vector machines. The relevance level of features are represented by ARD (automatic relevance determination) parameters, which are optimized by maximizing the model evidence in the Bayesian framework. The features are ranked in descending order using the optimal ARD values, and then forward selection is c...

Feature selection has a direct impact on text categorization. Most existing algorithms are based on document level, and they haven't considered the influence of term frequency on text categorization. Based on these, we put forward a feature selection approach, FSATD, based on term distributions in the paper. In our proposed algorithm, three critical factors which are term frequency, the inter-c...

In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or ...

Node cooperation is considered in this paper in the existence of two relaying protocols i.e. “Amplify and Forward (AF)” also called “Store and Forward (SF)” and the second relaying protocol is “Decode and Forward”. The two stated protocols are different from each other and perform different operations on received data before retransmitting to next hop. Also the selection of each protocol is var...

We propose a probabilistic model class for the analysis of three-way count data, motivated by studying the subjectivity of language. Our models are applicable for instance to a data tensor of how many times each subject used each term in each context, thus revealing individual variation in natural language use. As our main goal is exploratory analysis, we propose hybrid bilinear and trilinear m...