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...

this article aims to provide a simple robust method to test the parameters of a normal population by using the new diagnostic tool called the “forward search” (fs) method. the most commonly used procedures to test the mean and variance of a normal distribution are student’s t test and chi-square test, respectively. these tests suffer from the presence of outliers. we introduce the fs version of...

In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and highly sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are difficult to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to ...

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 ...

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with t...

Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...

electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...