Evolutionary Feature Selection for Texture Classification Using Multiwavelets
نویسندگان
چکیده
In this paper, we use multiwavelet transforms to perform texture classification on twelve Brodatz textures. To increase the correct classification rate, feature selection is considered. Here we use the coevolutionary algorithm rather than the genetic algorithm (GA) which is widely used in many researches to accomplish the training phase of feature selection. In the classification phase, the mean and variance of the selected features are calculated with the leave-one-out algorithm to obtain the results. From the results of our experiment, we can see that classification using our method is very efficient.
منابع مشابه
Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملTexture Image Segmentation Based on Wavelet Signatures
In this paper, we formulate the segmentation problem based on textured images as an optimization problem, and adapt evolutionary algorithms for the selection in a wavelet feature space. The purpose here is to demonstrate the efficiency of GHM multiwavelets in texture discrimination with respect to D4 scalar wavelets. Comparative studies suggest that the former transform features may contain mor...
متن کاملEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...
متن کاملOnline Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کاملTexture Classification of Diffused Liver Diseases Using Wavelet Transforms
Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure. The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There are some approaches to develop a reliable noninvasive method of evaluating histological changes in sonograms. The main characteristic used to distinguish between the normal...
متن کامل