Classification with thousands of classes and a large number of features is often
computationally intractable. The presence of irrelevant features can decrease the …

Feature selection is used in choosing relevant features that contribute to the predictive
power of a machine learning model. Irrelevant features tend to decrease model accuracy …

In this work we present SPICY (SPecialized Classification sYstem) application for a
supervised data analysis (feature selection, classification, model validation and model …

Text data usage has increased rapidly and simultaneously resulted in setbacks, such as
high dimensionality of text data becoming a prominent problem. Hence, this study aimed to …

Feature selection methods for cancer classification are aimed to overcome the high
dimensionality of the biomedical data which is a challenging task. Most of the feature …

Аннотация Предложен алгоритм формирования системы эффективных
классификационных характеристик, основанный на концепции усеченного перебора и …

An algorithm for the formation of a set of effective classification features, based on the
truncated search concept and the use of the information about individual classification …

Modern multi-omics studies introduce a major challenge for biomedical data storage,
processing and integration. The ability to utilize multiple different measurement techniques …

EEG signal suffers from some problem which leads to difficult during diagnosis of diseases.
This paper is presented a new approach for diagnosing epilepsy disease through EEG …

Journalof Educationfor Pure Science- University of Thi-Qar Epileptic Detection Based On EEG
Signal Using Graph Index Complexity Page 1 Journalof Educationfor Pure Science …