Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF (Independent Feature Elimination- by C-Correlation and F-Correlation) Measures

Diagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms

Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

Evaluating The Usability of a Web Software and Determining its Correlation with Fatigue and Burnout Factor in Office Personnel of a Health Center in Tehran City

 Introduction: The importance of assessing and identifying ergonomic risk factors in the workplace among office workers has increased with the increase in occupational tasks of computer and increasing the risk of physical and mental disorders among its users. One of these factors is the usability of computer hardware and software. The purpose of this study was to evaluate the applicability of o...

A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters

Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...

Seven Techniques for Data Dimensionality Reduction Missing Values, Low Variance Filter, High Correlation Filter, PCA, Random Forests, Backward Feature Elimination, and Forward Feature Construction

A Geometric View of Similarity Measures in Data Mining

The main objective of data mining is to acquire information from a set of data for prospect applications using a measure. The concerning issue is that one often has to deal with large scale data. Several dimensionality reduction techniques like various feature extraction methods have been developed to resolve the issue. However, the geometric view of the applied measure, as an additional consid...