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The intelligent analysis of Databases may be affected by the presence of unimportant features, which motivates the application of Feature Selection. By treating this task as a search and optimization process, it is possible to use the synergy between Genetic Algorithms and Multi-objective Optimization to carry out the search for (quasi) optimal subsets of features considering possible conflicting...
In this research a hybrid feature selection technique based on genetic and simulated annealing algorithms is proposed. this approach is evaluated by using Bayesian classifier on a dataset of hand-printed Farsi characters which includes 100 samples for each 33 hand-printed characters. The acquired results have been improved by correction of Simulated Annealing through considering two minimum and maximum...
One major task of online writeprint identification is to select the key features for representing the writeprint and facilitating the classifier built by using only the selected feature subset. In this study, we develop a hybrid genetic algorithm: RelieF Fed Genetic Algorithm (RFGA) which incorporates feature weight information produced by using RelieF as the heuristic to identity the key features...
The load of wet ball mill is a key parameter for grinding process, which affects the productivity, quality and energy consumption. A new soft sensor approach based on the mill shell vibration signal is proposed in this paper. As the frequency domain signal contains more evidently information than time domain, the power spectral density (PSD) of the vibration signal was obtained via fast Fourier transform...
The main criticism of employing genetic algorithms in data mining applications is local convergence and their long running time particularly for large datasets with large number of attributes. One solution to this problem is giving a filtering bias to initial population such that more relevant attributes get initialized with higher probability as compared to not so important attributes with respect...
We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across multiple regions of the...
Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction. Straightforward procedures for prototype selection as well as feature selection have been used for this...
In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational...
The Internet's numerous benefits have always been coupled with shortcomings due to the abuses of online anonymity. Writeprint identification is a technique to identify individuals based on textual identity cues people leave behind online messages. Character n-gram is one of the most effective approaches to identify writeprint according to previous research. In this study, we propose a variable length...
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration...
Steganalysis has emerged as an important branch in information forensics. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviors, optimizing the performance of steganalysers becomes an important open problem. This paper is aimed at increasing the performance of the steganalysers in through feature selection thereby reducing the computational...
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses...
A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI...
Feature selection is the basic of the state evaluation and fault diagnosis, and it is difficult to get the reasonable token feature. In the paper, an intelligence method of state feature system establishment and optimize has been studied. Diesel engine was illustrated in the paper, the signal of diesel engine has been collected when the piston ring and airtight ring working at different state, then...
Genetic algorithm (GA) applied to feature selection and model optimization improved the performance of robust mathematical models such as Bayesian-regularized neural networks (BRANNs) and support vector machines (SVMs) on different drug design datasets. The selection of optimum input variables and the adjustment of network weights and biases to optimum values to yield generalizable predictors were...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on Genetic Algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
In this paper the adaptive feature selection model (AFSM) which is based on two layers, adaptive and multi-classes JM distance is studied. During the process of crop identification using remote sensing (RS) image classification, it is the effective way to improve the classification accuracy that the feature is proper treated. Firstly, with MODIS data as examples, the extracted spectral characteristics...
Feature extraction is an important issue for analysis of gene expression microarray data, of which principle component analysis (PCA) is one of the frequently used methods, and in the previous works, the top several principle components are selected for modeling according to the descending order of eigenvalues. In this paper, we argue that not all the first features are useful, but features should...
This paper presents a generic features selection method and its applications on some document analysis problems.The method is based on a genetic algorithm (GA), whose fitness function is defined by combining Adaboot classifiers associated with each feature. Our method is not linked to a classifier achieving the final recognition task; we have used a combination of weak classifiers to evaluate a subset...
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