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Abstract: By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed. The method uses the granular agent to denote the set of some examples that have similar attributions and the knowledge base guides the evolution of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent, respectively. Finally, some classification rules are extracted from granular agents by some strategy to forecast the sort of new data. Empirical study contains UCI data sets, KDDCUP99 data sets and remote image recognition. The test results show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI data sets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance. At the same time, some Gaussian White Noise attributes are added to these UCI data sets and the results show GACE has some anti-noise abilities. To test the scalability of GAEC, two functions along two dimensions, the number of training examples and the number of attributes are used. Also, GAEC are applied to some real world fields, intrusion detection system and remote sensing image recognition. The experiments for KDDCUP99 verify GAEC has capability to deal with massive data in real world and good predicting capability for unknown type data. At last, the accuracy rate of GAEC is also good for the remote sensing image recognition. [Copyright &y& Elsevier]

The spectral–spatial classification methods have improved the classification accuracy of hyperspectral imagery dramatically, however, the traditional random sampling method causes the high overlap between training samples and testing samples. So, the improvement of the accuracy is partly due to the overlap of these samples. In this paper, a non-overlapping classification framework for hyperspectral imagery based on the distance of regularized nearest points (NORNP) is proposed. First, a novel sampling method named controlled random sampling is adopted to generate the training sets. The controlled random sampling can reduce the overlap between the training and testing samples a lot. The training samples with the same label will be used to form a set. Then, the superpixel segmentation method is adopted to divide the hyperspectral imagery into many smoothed small regions. Finally, all sets are represented as regularized affine hulls (RAHs). Every two RAHs can automatically choose two regularized nearest points (RNP) by an iterative algorithm. The distance between RNPs is used as the sets distance. The label of testing set is determined by the minimum distance to each training set. Experimental results based on three real hyperspectral data sets demonstrate that the proposed set-to-set classification method can provide better classification results when training samples and testing samples are less overlapped. • Non-overlapping sampling is used for the classification of Hyperspectral Imagery. • Non-overlapping classification with regularized nearest points distance is proposed. • Removes the influence of the overlap between training and testing samples. • Classification accuracy of NORNP is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To preserve the sparsity structure in dimensionality reduction, sparsity preserving projection (SPP) is widely used in many fields of classification, which has the advantages of noise robustness and data adaptivity compared with other graph based method. However, the sparsity parameter of SPP is fixed for all samples without any adjustment. In this paper, an improved SPP method is proposed, which has an adaptive parameter adjustment strategy during sparse graph construction. With this adjustment strategy, the sparsity parameter of each sample is adjusted adaptively according to the relationship of those samples with nonzero sparse representation coefficients, by which the discriminant information of graph is enhanced. With the same expectation, similarity information both in original space and projection space is applied for sparse representation as guidance information. Besides, a new measurement is introduced to control the influence of each sample's local structure on projection learning, by which more correct discriminant information should be preserved in the projection space. With the contributions of above strategies, the low-dimensional space with high discriminant ability is found, which is more beneficial for classification. Experimental results on three datasets demonstrate that the proposed approach can achieve better classification performance over some available state-of-the-art approaches. • Adaptive sparse graph learning is proposed to learn more discriminant sparse graph. • Local structure information is used as guide information for sparse representation. • The influence of the individual difference on projection learning is investigated. [ABSTRACT FROM AUTHOR]

In recent years, the panchromatic (PAN) and the multispectral (MS) remote sensing images classification has become a research hotspot. In this paper, we propose a spatial-channel progressive fusion residual network (SCPF-ResNet) for multi-resolution remote sensing classification. Firstly, for the inputs of the proposed network, the interactive data fusion strategy (IDFS) combines generalized-intensity-hue-saturation (GIHS), and discrete wavelet transform (DWT) to interfuse patch pairs of the PAN and the MS images, so as to increase the similarity between them, thus reduce the difference in information between them. Secondly, for the branches of feature extraction, we design an adaptive spatial attention module (ASA-Module) and an adaptive channel attention module (ACA-Module) to strengthen spatial features from both larger-sized with smaller-sized targets and spectral features among channels. Finally, we insert the ASA-Module and ACA-Module into the residual modules to form a triple-branch network and use the common spatial-channel features extracted by the Fusion_Branch to gradually enhance the pure independent features extracted by the PAN_Branch and the MS_Branch, respectively. The experimental results indicate that SCPF-ResNet can achieve competitive performance. • An interactive data fusion strategy is proposed. • Two adaptive attention modules to highlight the advantages of two different data. • A triple-branch progressive fusion residual network for classification is proposed. [ABSTRACT FROM AUTHOR]

• A dual branch attention fusion deep network for classification is proposed. • An adaptive center offset sampling strategy for each patch is proposed. • Two attention based modules to highlight the advantages of two different data. • The feature level fusion is achieved by the proposed network. In recent years, with the diversification of acquisition methods of very high resolution panchromatic (PAN) and multispectral (MS) remote sensing images, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of data–driven deep learning, we design a dual–branch attention fusion deep network (DBAF–Net) for the multiresolution classification. It aims to integrate the feature–level fusion and classification into an end–to–end network model. In the process of establishing a training sample library, unlike the traditional pixel–centric sampling strategy with fixed patch size, we propose an adaptive center–offset sampling strategy (ACO–SS), which allows each patch to adaptively determine the neighborhood range by finding the texture structure of the pixel to be classified. And the neighborhood range is not symmetrical with this pixel, we expect to capture the neighborhood information that is more conducive to its classification. In network structure, based on the captured patches by ACO–SS, we design a spatial attention module (SA–module) for PAN data and a channel attention module (CA–module) for MS data, thus highlighting the spatial resolution advantages of PAN data and the multi–channel advantages of MS data, respectively. Then these two features are interfused to improve and strengthen the fusion features in both spatial and channel. The quantitative and qualitative experimental results verify the robustness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]