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Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods...
The statistical Haralick features from the texture description methods GLCM, GLDM, SRDM, NGLCOM, NGLDM and Run-length features from the texture description method GLRLM are widely used to extract features in mammogram images for analysis and classification of abnormality. In this paper a novel feature extraction method based on spectral shape is proposed for classification of abnormality in mammogram...
Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods...
Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. In this work, Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in...
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
This paper proposes a new classification method based on association rule mining. This association rule-based classifier is experimented on a real dataset; a database of medical images from MIAS database. The proposed system employs Ant-Miner metaheuristic algorithm for extracting knowledge in the form of decision rules using texture features extracted with the help of co-occurrence matrices. These...
VISTA, a dynamic information visualization system, which allows the user to interactively observe potential clusters in a series of continuously changing visualizations, incorporates the algorithmic clustering results, and serves as an effective validation and refinement tool for irregularly shaped clusters. The validation and visual clustering is performed by tuning the parameter alpha for dominating...
Genetic algorithm (GA) and Ant colony optimization (ACO) algorithm are proposed for feature selection, and their performance is compared. The spatial gray level dependence method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer backpropagation network hybrid with ant colony optimization (BPN-ACO) for classification. And the receiver operating characteristic (ROC)...
Microcalcification on X-ray mammogram is a significant mark for early detection of breast cancer. Texture analysis methods can be applied to detect clustered microcalcification in digitized mammograms. In order to improve the predictive accuracy of the classifier, the original number of feature set is reduced into smaller set using feature reduction techniques. In this paper rough set based reduction...
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