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Condition monitoring is a vital task in the maintenance of industry machines. This paper proposes a reliable condition monitoring method using a genetic algorithm (GA) which selects the most discriminate features by taking a transformation matrix. Experimental results show that the features selected by the GA outperforms original and randomly selected features using the same k-nearest neighbor (k-NN)...
The dimensionality reduction by feature selection is one of the fundamental steps in the pre-processing data stage in the intelligent data analysis. Feature selection (FS) literature embodies a wide spectrum of algorithms, methods and strategies, but mostly all fall into two classes, the well known wrappers and filters. The decision of which feature or variable is selected or discarded from the best...
Feature selection (FS) and classifier design (CD) are two basic stages in the construction of a classification system. Typically, both tasks have been studied separately in literature. FS aims to remove irrelevant and redundant features whereas CD generates a prediction rule for classifying patterns whose class is unknown. Despite the relationship between FS and CD with radial basis function networks...
In our previous study, a grouping-geneticalgorithm- based (GGA-based) attribute clustering process has been proposed for grouping features. In this paper, we further improve its performance and propose a center-based GGA for attribute clustering (CGGA). A new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
Correctly labelled datasets are commonly required. Three particular scenarios are highlighted, which showcase this need. When using supervised Intrusion Detection Systems (IDSs), these systems need labelled datasets to be trained. Also, the real nature of the analysed datasets must be known when evaluating the efficiency of the IDSs when detecting intrusions. Another scenario is the use of feature...
In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which...
In order to increase the accuracy of abnormal event detection in crowd video surveillance, this paper proposes a novel hybrid optimization of feature selection and support vector machine (SVM) training model based on genetic algorithm. For reducing dimensions of multi-feature, we propose an adaptive genetic simulated annealing algorithm (ASAGA) feature selection method. The ASAGA takes advantage of...
The increase use of social media and Web 2.0 are daily drawing more people to participate and express their point of views about a variety of subjects. However, there are a huge number of comments which are offensives and sometimes non-politically corrects and so must be hindered from coming up online. This is pushing the services providers to be more careful with the contents they publish to avoid...
Content generated by users is one of the most interesting phenomena of published media. However, the possibility of unrestricted edition is a source of doubts about its quality. This issue has motivated many studies on how to automatically assess content quality in collaborative web sites. Generally, these studies use machine learning techniques to combine large number of quality indicators into a...
This paper proposes to use Genetic algorithm for optimizing the best Eigen vectors to improve the recognition accuracy of Modular image Principal Component Analysis (MIPCA) for face recognition. Modular Image PCA has been proved to be efficient in extracting features for recognizing face invariant to large expression. It is important to note that all the extracted features are not efficient and required...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) associated to a classifier (linear SVM) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. The impact of the number of selected bands on classification accuracy was obtained thanks to SFFS, while a band importance measure...
Recent developments in the field of-omics technologies brought great potential for conducting biomedical research in very efficient manner, but also raised a plethora of new computational challenges to be addressed. Extremely high dimensionality accompanied with poor signal-to-noise ratio and small sample size of data resulting from high-throughput experiments pose previously unprecedented problem,...
With the rapid development of the Computer Science and Technology, It has become a major problem for the users that how to quickly find useful or needed information. Data mining can be seen as an area of artificial intelligence that seeks to extract information or patterns from large amounts of data stored in databases. Recent researches on feature selection have been conducted in an attempt to find...
This paper presents to the improvement of the Significant Matrix [1] that works along with Genetic Algorithm in feature selection of appropriate data for a decision tree structure. This work proposes the reduction of time that cut off the Genetic Algorithm's work times. The new method is proposed in the name “Significant Matrix 2” which is calculated from the relationship between categorical data...
Breast cancer continues to be one of the most common cancers, and survival rates critically depend on its detection in the initial stages. Several studies have demonstrated the benefits and potential of using CAD (Computer-Assisted Diagnosis) systems to help specialists in their clinical interpretation of mammograms. CAD is based essentially on 2 main steps: Extraction of pertinent features and classification...
Manufacturing data is an important source of knowledge that can be used to enhance the production capability. The detection of the causes of defects may possibly lead to an improvement in production. However, the production records generally contain an enormous set of features. It is almost impossible in practice to monitor all features at once. This research proposes the feature reduction technique,...
Ensemble systems are composed of a set of individual classifiers, organized in a parallel way, that receive the input patterns and send their output to a combination method, which is responsible for providing the final output of the system. The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among...
Genetic Algorithms in combination with Artificial Neural Networks have been used to solve optimization problems in several domains. In this paper, an evolutionary algorithm consisting of an Artificial Neural Network and a Genetic Algorithm is presented for predicting the asthma outcome in children under the age of five. The most cases of asthma begin during the first years of life, thus the early...
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative...
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