ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES

Mehran Taghipour-Gorjikolaie, Seyyed Mohammad Razavi, Javad Sadri

Abstract:

Automatic classification of objects is one of the most important tasks in engineering
and data mining applications. Although using more complex and advanced
classifiers can help to improve the accuracy of classification systems, it can be
done by analyzing data sets and their features for a particular problem. Feature
combination is the one which can improve the quality of the features. In this paper,
a structure similar to Feed-Forward Neural Network (FFNN) is used to generate an
optimized linear or non-linear combination of features for classification. Genetic
Algorithm (GA) is applied to update weights and biases. Since nature of data sets
and their features impact on the effectiveness of combination and classification
system, linear and non-linear activation functions (or transfer function) are used
to achieve more reliable system. Experiments of several UCI data sets and using
minimum distance classifier as a simple classifier indicate that proposed linear and
non-linear intelligent FFNN-based feature combination can present more reliable
and promising results. By using such a feature combination method, there is no
need to use more powerful and complex classifier anymore.

TL;DR: A method for enriching and combining features to improve classification accuracy

Keywords: Classification, Feature Combination, Feature Mapping, Feed-Forward Neural Network, Genetic Algorithm, Linear Transfer Function, Non-Linear Transfer Function