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In this paper, we propose an novel instance selection algorithm and an improved adaptive neuro-fuzzy algorithm for Computer Aided Detection (CAD) of mammography. Firstly, the X-Ray images are partitioned into blocks. Secondly, the texture model is built for all negative packages instances. The distances from the unknown instances to the average model of negative packages are calculated. The instance...
This paper proposes a new technique for hyperspectral band selection from the spectral similarity perspective. Through a newly defined measure for band subset discriminativeness, class-specific important bands are retained which can preserve the spectral similarity of the samples from the same class and narrow down candidate band subset for the following search procedure. Then optimal search is performed...
A novel algorithm which combines clustering analysis and SVM is proposed for classification. Specifically, based on the conglomeration and decentralization characteristics of the positive and negative samples, we present a new type of support vector machine called Clustered Grouping Support Vector Machine or GC-SVM. After clustering training, the samples are divided into different groups, then a series...
In this paper, an algorithm for texture analysis of clustered calcification based on statistical texture models is proposed. The prior knowledge of both normal and lesion training samples are incorporated into statistical texture models separately. Specifically, beside texture analysis of the lesion tissues, and the resultant statistical parameters can also be used for unknown sample representation...
When solving the problem in computer assisted detection by the approach of pattern recognition, the lesion data always exhibited high-dimensional and inhomogeneous, which makes most of the traditional classifiers can not performance very well. In this paper, a novel approach based on the dynamic feature subset selection and the EM algorithm with Naive Bayesian classifier integration algorithm (DSFS+EMNB)...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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