A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis | Zendy

Marwa Trabelsi | Zendy; Nida Meddouri | Zendy; Mondher Maddouri | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis

Author(s) -

Marwa Trabelsi,

Nida Meddouri,

Mondher Maddouri

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.227

Subject(s) - computer science , classifier (uml) , feature selection , artificial intelligence , word error rate , pattern recognition (psychology) , machine learning , data mining

The high dimension of data makes difficult to train and test many classification methods. This work aims to present a new filter Feature Selection Method, called H-Ratio, which can identify pertinent features from data. This method improves results of two previous works focusing on nominal classifiers based on Formals Concepts Analysis. The evaluation of H-Ratio shows that this method performs nominal classifiers processing. Our method has an error rate of 5% (~7% relative improvement over a supervised classification method).

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research