Hadoop neural network for parallel and distributed feature selection

Hadoop neural network for parallel and distributed feature selection

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various ...

A New Framework for Distributed Multivariate Feature Selection

Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...

A Parallel Genetic Algorithm Based Method for Feature Subset Selection in Intrusion Detection Systems

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...

A fuzzy neural network for pattern classification and feature selection

Parallel and Distributed Systems for Constructive Neural Network Learning

A construct ive learning algori thm dynamical ly creates a problem-specific neural ne twork architecture rather t h a n learning o n a pre-specified architecture. W e propose a parallel vers ion of our recently presented construct ive neural ne twork learning algori thm. Parallelization provides a computat ional speedup by a fact o r of O ( t ) where t is t h e number of training ezamples. Dist...