Keywords- feature selection; Data mining; filter; wrapper; hybrid I. INTRODUCTION In recent years, data collected for various research purposes are much larger. Such data set may consists of thousands of instances (records) and each of which may be represented by hundreds or thousands of features (attributes or variables) [1].
What is Feature Selection. Feature selection is also called variable selection or attribute selection. It is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on.
Keywords: Feature Selection, Data mining, Filter approach, Wrapper approach I. INTRODUCTION Due to availability of large amounts of data from the last few decades, the analysis of data becomes more difficult manually. So the data analysis should be done computerized through Data Mining. Data Mining helps in fetching the hidden
One of the characteristics of recent problems can be referred to the great number of features that have led to slowing down the classification systems, decreased efficiency and rising the costs of such systems. In recent years, feature selection
One of the most interesting and well written paper I have read regarding data mining is certainly "An Introduction to Variable and Feature Selection" (Guyon and Elisseeff, 2003).
Feature selection is important for several reasons. First, it improves the interpretability of the classifier, in the sense that it highlights the features that are really used in the classification.
Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications.
Abstract. Feature Selection methods in Data Mining and Data Analysis problems aim at selecting a subset of the variables, or features, that describe the data in order to obtain a more essential and compact representation of the available information.
Feature Selection. Feature Selection (.pdf) . Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm.The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions.
About the Book. Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications.This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.
Notes Abstract: Analysing and extracting useful information from high dimensional dataset challenges the frontiers of statistical tools and methods.
A Multiobjective Genetic Algorithm for Feature Selection in Data Mining Venkatadri.M, Srinivasa Rao.K Dept of CSE &IT, Jawaharlal Nehru Institute of Technology, Hyderabad
This paper presents an automatic Heart Disease (HD) prediction method based on feature selection with data mining techniques using the provided symptoms and clinical information assigned in the patients dataset. Data mining which allows the
Feature Selection in Data Mining YongSeog Kim, W. Nick Street, and Filippo Menczer, University of Iowa, USA INTRODUCTION Feature selection has been an active research area in …
