Abstract
As of late, Feature extraction in email classification assumes a vital part. Many Feature extraction algorithms need more effort in term of accuracy. In order to improve the classifier accuracy and for faster classification, the hybrid algorithm is proposed. This hybrid algorithm combines the Genetics Rough set with blind source separation approach (BSS-GRF). The main aim of proposing this hybrid algorithm is to improve the classifier accuracy for classifying incoming e-mails.
Highlights
BLIND SOURCE SEPARATION ALGORITHMIn blind source separation (BSS), numerous perceptions are done by an array of words are handled so as to recover the beginning blending of the source signals
Abstract—as of late, Feature extraction in email classification assumes a vital part
It applies a series of genetic operators like selection, crossover, and mutation to a group of chromosomes where every chromosome give us answer to a problem
Summary
In blind source separation (BSS), numerous perceptions are done by an array of words are handled so as to recover the beginning blending of the source signals. Blind source separation (BSS) is the strategy that anybody can separate the first message or information from their mixtures without any learning about the blending methodology, yet utilizing some measurable properties of inactive or unique source message. The perception of blind source separation is related to independent component analysis [3]. Some application of blind source separation is geophysical data processing, data mining, biomedical signal analysis and wireless communications [2]. It is expected that each input signal is a linear combination of some statistically independent source [1]. M and a are evaluated by solving the following:. Where the observed A of a in each step of the algorithm is the solution of the following: A= arg min a ||x- Ma||2 ,
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