Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a lo...

متن کامل

Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

متن کامل

Selecting feature subset for high dimensional data via the propositional FOIL rules

Feature interaction is an important issue in feature subset selection. However, most of the existing algorithms only focus on dealing with irrelevant and redundant features. In this paper, a propositional FOIL rule based algorithm FRFS, which not only retains relevant features and excludes irrelevant and redundant ones but also considers feature interaction, is proposed for selecting feature su...

متن کامل

Retrieval of Optimal Subspace Clusters Set for an Effective Similarity Search in a High-Dimensional Spaces

High dimensional data is often analysed resorting to its distribution properties in subspaces. Subspace clustering is a powerfull method for elicication of high dimensional data features. The result of subspace clustering can be an essential base for building indexing structures and further data search. However, a high number of subspaces and data instances can conceal a high number of subspace...

متن کامل

Similarity Search in High-Dimensional Data Spaces

This paper summarizes analytical and experimental results for the nearest neighbor similarity search problem in high-dimensional vector spaces using some kind of space-or data-partitioning scheme. Under the assumptions of uniformity and independence of data, we are able to formally show and to demonstrate that conventional approaches to the nearest neighbor problem degenerate if the dimensional...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Applied Mathematics

سال: 2013

ISSN: 1110-757X,1687-0042

DOI: 10.1155/2013/590614