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 quick review of engineering problems reveals importance of classification and its application in medicine, mechanical and electrical engineering, computer science, power systems and so on. Some of its important applications include disease diagnosis using classification methods to diagnosis Thyroid (Temurtas (2009)), Parkinson BID4 ) and Alzheimers disease BID7 ); or fault detection in power systems such as BID6 ) which uses classification methods to detect winding fault in windmill generators; BID12 ) using neuro-fuzzy based classification method to detect faults in AC motor; and also fault detection in batch processes in chemical engineering BID22 ). In all classification problems extracting useful knowledge and features from data such as image, signal, waveform and etcetera can lead to design efficient classification systems. As extracted data and their features are not usually suitable for classification purpose, two major approaches can be substituted. First approach considers all the classifiers and tries to select effective ones, even if their complexity and computational cost are increased. Second approach focusing on the features, enhances the severability of data, and then uses improved features and data for classification.Feature combination is one of the common actions used to enhance features. In classic combination methods, deferent features vectors are lumped into a single long composite vector BID19 ). In some modern techniques, in addition to combination of feature vectors, dimension of feature space is reduced. Reduction process can be done by feature selection, transmission, and projection or mapping techniques, such as Linear Discriminate Analysis (LDA), Principle Component Analysis (PCA), Independent Component Analysis (ICA) and boosting BID19 ). In more applications, feature combination is fulfilled to improve the efficiency of classification system such as BID3 ), that PCA and Modular PCA (MPCA) along Quad-Tree based hierarchically derived Longest Run (QTLR) features are used to recognize handwritten numerals as a statistical-topological features combination. The other application of feature combination is used for English character recognition, here structure and statistical features combine then BP network is used as a classifier ). Feature combination has many applications; however before using, some questions should be answered: which kind of combination methods is useful for studied application and available data set. Is reduction of feature space dimension always useful? Is linear feature combination method better than non-linear one?In this paper, using structure of Feed-Forward Neural Network (FFNN) along with Genetic Algorithm (GA) as a powerful optimization algorithm, Linear Intelligent Feature Combination (LIFC) and Non-Linear Intelligent Feature Combination (NLIFC) systems is introduced to present adaptive combination systems with the nature of data sets and their features. In proposed method, original features are fed into semi-FFNN structure to map features into new feature space, and then outputs of this intelligent mapping structure are classified by minimum distance classifier via cross-validation technique. In each generation, weights and biases of semi-FFNN structure are updated by GA and correct recognition rate (or error recognition rate) is evaluated.In the rest of this paper, overview of minimum distance classifier, Feed-Forward Neural Network structure and Genetic Algorithm are described in sections2, 3and 4, respectively. In section 5, proposed method and its mathematical consideration are presented. Experimental results, comparison between proposed method and other feature combinations and classifiers using the same database are discussed in section 6. Eventually, conclusion is presented in section 7.
A method for enriching and combining features to improve classification accuracy
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