title: ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES
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.
authors: Mehran Taghipour-Gorjikolaie Seyyed Mohammad Razavi Javad Sadri
hIndex: Javad Sadri 15