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Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted...
the class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic...
Generally, information system handles huge volume of dataset. Classifiers provide poor performance when such dataset are feed into it for categorization due to their high dimension. The most important attributes are extracted from the dataset prior to classification for efficient classifier design. It is also true that there may be many classifiers of a particular system, some provide better accuracy...
Text feature acquiring is the key to construct the classifier to classify the text, According to the problem that the text dimension of the original feature vector is reduced and accurate, put forward a text feature acquiring algorithm based on co evolution, the algorithm uses genetic algorithm optimization performance and co evolution can implement multiple population mutual evaluation and competition,...
Automatic image annotation (AIA) for a huge number of images is one of the most difficult challenging topics for researchers in the last two decades. For labeling images accurately, more various features containing low-level image features, textual tags of images have been extracted so far; however, not whole features give useful information for each conception. Feature selection as one of the important...
A new method of Michigan and Pittsburgh approaches combination for fuzzy classifier design with evolutionary algorithms is presented. Fuzzy classifier design includes of four stages. The first stage is standard fuzzification. The second one is a special procedure of initial rules forming with a priori information from a learning sample. At the third stage Michigan method is applied and it provides...
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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