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.
A method for enriching and combining features to improve classification accuracy