The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The pap...

Dimensionality Reduction process is a means to overcome curse of dimensionality in general. When all features are available together, it is a way to extract knowledge from a population in a big feature space. On the contrary, dimensionality reduction is intriguing when update to feature space is streaming and the question arises whether one could reduce the feature space as and when the feature...

Data analysis in cardiovascular diseases is difficult due to large massive of information. All of features are not impressive in the final results. So it is very important to identify more effective features. In this study, the method of feature selection with binary cuckoo optimization algorithm is implemented to reduce property. According to the results, the most appropriate classification fo...

Abstract In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models trained with four different feature sets their performances evaluated terms accuracy F-measure metrics. While first experiments directly used own stock features as model inputs, second utilized reduc...

Thee rapid advances in hyperspectral sensing technology have made it possible to collect remote sensing data in hundreds of bands. However, the data analysis methods which have been successfully applied to multispectral data are often limited to achieve satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hug...

This paper aims at finding suitable parameters for dysphonic voice analysis and classification. Moreover, a non-linear noise reduction scheme is proposed, for voice correction. Typical quantities from chaos theory and some conventional ones are evaluated, in order to provide entries for feature vectors in a feature space. Geometric signal separation is applied for voice classification, by means...

A coarse and a fine dendrite structure were produced using the newly developed DS-superalloy ExAl7 ( IN792 with 2-3 wt.-% Re) by changing the conditions of directional solidification. The two microstructures differed in their primary dendrite arm spacing by a factor of 1.6. During aging, very fine TCP phase precipitates (length approximately 20 m, volume fraction ~ 1 %) were precipitated in the...

In order to preserve the integrity of edge and detail information in the underwater image, a NSCT de-noising method based on Non-local means with modified parameter is proposed. Since NSCT has the feature of translation invariance, it is used to decompose the underwater image in multi-scale and multi-direction. For the noise and detail information are normally distributed in the high frequency ...

In this paper, we propose a two-stage variable selection procedure for high dimensional quantile varying coefficient models. The proposed method is based on basis function approximation and LASSO-type penalties.We show that the first stage penalized estimator with LASSO penalty reduces the model from ultra-high dimensional to a model that has size close to the true model, but contains the true ...