基于遗传算法的结肠癌基因选择与样本分类

提出了一种基于两轮遗传算法的用于结肠癌微阵列数据基因选择与样本分类的新方法。该方法先根据基因的Bhattacharyya距离指标过滤大部分与分类不相关的基因,而后使用结合了遗传算法和CFS（Correlation-based Feature Selection）的GA/CFS方法选择优秀基因子集,并存档记录这些子集。根据存档子集中基因被选择的频率选择进一步搜索的候选子集,最后以结合了遗传算法和SVM的GA/SVM从候选基因子集中选择分类特征子集。把这种GA/CFS-GA/SVM方法应用到结肠癌微阵列数据,实验结果及与文献的比较表明了该方法效果良好。     

基于最大信息系数的ReliefF和支持向量机交互的自动特征选择算法

为解决特征选择ReliefF算法在利用欧氏距离选取近邻样本过程中,算法稳定性差以及选取的特征子集分类准确率低的问题,提出了一种利用最大信息系数（MIC）作为近邻样本选择标准的MICReliefF算法;同时,以支持向量机（SVM）模型的分类准确率作为评价指标,并多次寻优,以自动确定其最优特征子集,从而实现MICReliefF算法与分类模型的交互优化,即MICReliefF-SVM自动特征选择算法。在多个UCI公开数据集上对MICReliefF-SVM算法的性能进行了验证。实验结果表明,MICReliefF-SVM自动特征选择算法不仅可以筛除更多的冗余特征,而且可以选择出具有良好稳定性和泛化能力的特征子集。与随机森林（RF）、最大相关最小冗余（mRMR）、相关性特征选择（CFS）等经典的特征选择算法相比,MICReliefF-SVM算法具有更高的分类准确率。     

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city,Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.     

基于双树复小波和灰度共生矩阵的遥感图像分割

提出了一种将双树复小波变换和灰度共生矩阵相结合描述遥感图像局部纹理特征并用于分割的方法。该方法采用双树复小波高频模值子带Gamma分布与Lognormal分布参数组合特征、灰度共生矩阵特征组成的联合纹理特征作为遥感图像每一像素特征,然后用Canberra距离进行相似性度量,最终通过聚类完成遥感图像分割。实验结果表明,该纹理特征提取方法可以有效地表征遥感图像的纹理,得到更为精确的遥感图像分割结果。     

.基于纹理和边缘的SAR图像SVM分类*

为实现SAR图像地物目标的有效分类,深入研究了基于灰度共生矩阵GLCM的四种纹理特征以及两个边缘特征。分析每个单独纹理或边缘特征在对SAR图像进行支持向量机SVM分类中对不同地物的分辨能力,选取不同的特征组合进行组合特征的SVM分类实验。对各种特征进行主成分分析PCA,并考察使用和不使用PCA两种情况下分类结果之间的差异。实验结果证明能量、边缘长度、对比度和相关度的特征组合在PCA作用下能够改善各类地物的分类精度,将总分类精度提高到90%以上。     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.     

Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis

The long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Co-occurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.     

Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery

Textural features of high-resolution remote sensing imagery are a powerful data source for improving classification accuracy because using only spectral information is not sufficient for the classification of objects with within-field spectral variability. This study presents the methods of using an object-oriented texture analysis algorithm for improving high-resolution remote sensing imagery classification, including wavelet packet transform texture analysis, the grey-level co-occurrence matrix (GLCM) and local spatial statistics. Wavelet packet transform texture analysis, with the method of optimization and selection of wavelet texture for feature extraction, is a good candidate for object-oriented classification. Feature optimization is used to reduce the data dimensions in combinations of textural sub-bands and spectral bands. The result of the classification accuracy assessment indicates the improvement of texture analysis for object-oriented classification in this study. Compared with the traditional method that uses only spectral bands, the combination of GLCM homogeneity and spectral bands increases the overall accuracy from 0.7431 to 0.9192. Furthermore, wavelet packet transform texture analysis is the optimal method, increasing the overall accuracy to 0.9216 using a smaller data dimension. Local spatial statistical measures also increase the classification total accuracy, but only from 0.7431 to 0.8088. This study demonstrates that wavelet packet and statistical textures can be used to improve object-oriented classification; specifically, the texture analysis based on the multiscale wavelet packet transform is optimal for increasing the classification accuracy using a smaller data dimension.     

基于综合特征的纹理分割方法

针对灰度共生矩阵只能在单一尺度对纹理进行分析的不足,结合离散框架小波变换产生的尺度共生矩阵与梯度变换图像的灰度共生矩阵,提出了一种具有多尺度分析特性的综合纹理特征提取算法,并利用该特征对纹理图像进行分割.仿真实验结果表明:与基于单一尺度特征的纹理分割方法相比,本文提出的算法能够提高纹理边界定位准确性,减少区域内像素错分,取得了较好的分割效果.     

基于NSCT-GLCM的CT图像特征提取算法

针对海量CT图像分割中特征提取的难题,提出一种非下采样轮廓变换(NSCT)和灰度共生矩阵(GLCM)相融合的CT图像特征提取算法。首先采用NSCT对CT图像进行多尺度、多方向分解,并采用GLCM提取子带图像的共生特征量,然后对共生特征量进行主成分分析,消除冗余特征量,构成多特征矢量,最后利用支持向量机完成多特征矢量空间的划分,实现CT图像分割。实验结果表明,NSCT-GLCM能够较好地提取CT图像特征,提高了CT图像分割准确率,可以为医生诊断提供辅助信息。     

A genetic algorithm-based method for feature subset selection

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.     

Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests

Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas. Integration of lidar-derived elevation information into image classification can considerably improve classification results. Additionally, traditional pixel-based classifiers have some limitations in regard to certain landscape and data types. In this study, we take advantage of current advances in object-based image analysis and machine learning algorithms to reduce manual image interpretation and automate feature selection in a classification process. A sequence of image segmentation, feature selection, and object classification is developed and tested by the data sets in two study areas (Mannheim, Germany and Niagara Falls, Canada). First, to improve the quality of segmentation, a range image of lidar data is incorporated in an image segmentation process. Among features derived from lidar data and aerial imagery, the random forest, a robust ensemble classifier, is then used to identify the best features using iterative feature elimination. On the condition that the number of samples is at least two or three times the number of features, a segmentation scale factor has no particular effect on the selected features or classification accuracies. The results of the two study areas demonstrate that the presented object-based classification method, compared with the pixel-based classification, improves by 0.02 and 0.05 in kappa statistics, and by 3.9% and 4.5% in overall accuracy, respectively.     

一种基于Curv-SAE特征融合的人脸降维和识别方法

相比于传统的降维算法,深度学习中的栈式自编码器(Stacked Autoencoder,SAE)能够有效地学习特征并实现高效降维,然而对输入特征极其敏感。第二代离散曲波变换(Discrete Curvelet Transform,DCT)能够提取出人脸的各向信息(包含边缘和概貌特征),确保SAE的输入特征充分,从而弥补了其不足。因此,提出了一种基于Curv-SAE特征融合的人脸识别降维算法,即对人脸图像进行DCT得到特征脸并将其作为SAE的输入特征进行训练,特征融合后将其输入到分类器中进行识别。在ORL和FERET人脸数据库上的实验表明,与小波变换相比,曲波的特征信息更丰富;与传统的降维算法相比,SAE的特征表达更充分且识别精度更高。     

Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer

In machine learning, image classification accuracy generally depends on image segmentation and feature extraction methods with the extracted features and its qualities. The main focus of this paper is to determine the defected area of mangoes using image segmentation algorithm for improving the classification accuracy. The Enhanced Fuzzy based K-means clustering algorithm is designed for increasing the efficiency of segmentation. Proposed segmentation method is compared with K-means and Fuzzy C-means clustering methods. The geometric, texture and colour based features are used in the feature extraction. Process of feature selection is done by Maximally Correlated Principal Component Analysis (MCPCA). Finally, in the classification step, severe portions of the affected area are analyzed by Backpropagation Based Discriminant Classifier (BBDC). Proposed classifier is compared with BPNN and Naive Bayes classifiers. The images are classified into three classes in final output like Class A –good quality mango, Class B-average quality mango, and Class C-poor quality mango. Finally, the evaluated results of the proposed model examine various defected and healthy mango images and prove that the proposed method has the highest accuracy when compared with existing methods.

     

基于灰度共生矩阵的特征构建及超折射滤除

超折射回波会严重干扰对天气雷达图像中强对流回波的识别。文中从分析超折射回波及强对流回波在雷达反射率图中的分布特点入手,在区域分割的基础上,生成各区域的灰度共生矩阵,将灰度共生矩阵中的元素划分成两个子集,分别用以构建出两个新的特征,即平缓度/跳变性,它们在超折射回波和强对流回波样本之间呈现出显著性差异,配合使用径向速度特征,站在尽量不损失强对流的角度形成能够克服特征缺值的分类决策树。测试结果表明：文中方法较目前业务上普遍使用的模糊逻辑分布式超折射地物识别法,对超折射的滤除率及对强对流云团的保有率更高,特别是文中方法在将对超折射和强对流的识别准确率从96.8%提高到97.9%的前提下,对强对流的滤除率从3.91%降低到0.21%。     

Binary black hole algorithm for feature selection and classification on biological data

Biological data often consist of redundant and irrelevant features. These features can lead to misleading in modeling the algorithms and overfitting problem. Without a feature selection method, it is difficult for the existing models to accurately capture the patterns on data. The aim of feature selection is to choose a small number of relevant or significant features to enhance the performance of the classification. Existing feature selection methods suffer from the problems such as becoming stuck in local optima and being computationally expensive. To solve these problems, an efficient global search technique is needed.Black Hole Algorithm (BHA) is an efficient and new global search technique, inspired by the behavior of black hole, which is being applied to solve several optimization problems. However, the potential of BHA for feature selection has not been investigated yet. This paper proposes a Binary version of Black Hole Algorithm called BBHA for solving feature selection problem in biological data. The BBHA is an extension of existing BHA through appropriate binarization. Moreover, the performances of six well-known decision tree classifiers (Random Forest (RF), Bagging, C5.0, C4.5, Boosted C5.0, and CART) are compared in this study to employ the best one as an evaluator of proposed algorithm.The performance of the proposed algorithm is tested upon eight publicly available biological datasets and is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Correlation based Feature Selection (CFS) in terms of accuracy, sensitivity, specificity, Matthews’ Correlation Coefficient (MCC), and Area Under the receiver operating characteristic (ROC) Curve (AUC). In order to verify the applicability and generality of the BBHA, it was integrated with Naive Bayes (NB) classifier and applied on further datasets on the text and image domains.The experimental results confirm that the performance of RF is better than the other decision tree algorithms and the proposed BBHA wrapper based feature selection method is superior to BPSO, GA, SA, and CFS in terms of all criteria. BBHA gives significantly better performance than the BPSO and GA in terms of CPU Time, the number of parameters for configuring the model, and the number of chosen optimized features. Also, BBHA has competitive or better performance than the other methods in the literature.     

Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( $p<0.05$ ). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.     

A novel ensemble-based wrapper method for feature selection using extreme learning machine and genetic algorithm

This paper presents a novel wrapper feature selection algorithm for classification problems, namely hybrid genetic algorithm (GA)- and extreme learning machine (ELM)-based feature selection algorithm (HGEFS). It utilizes GA to wrap ELM to search for the optimum subsets in the huge feature space, and then, a set of subsets are selected to make ensemble to improve the final prediction accuracy. To prevent GA from being trapped in the local optimum, we propose a novel and efficient mechanism specifically designed for feature selection problems to maintain GA’s diversity. To measure each subset’s quality fairly and efficiently, we adopt a modified ELM called error-minimized extreme learning machine (EM-ELM) which automatically determines an appropriate network architecture for each feature subsets. Moreover, EM-ELM has good generalization ability and extreme learning speed which allows us to perform wrapper feature selection processes in an affordable time. In other words, we simultaneously optimize feature subset and classifiers’ parameters. After finishing the search process of GA, to further promote the prediction accuracy and get a stable result, we select a set of EM-ELMs from the obtained population to make the final ensemble according to a specific ranking and selecting strategy. To verify the performance of HGEFS, empirical comparisons are carried out on different feature selection methods and HGEFS with benchmark datasets. The results reveal that HGEFS is a useful method for feature selection problems and always outperforms other algorithms in comparison.     

Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery

ABSTRACT

The main objective of this study is to apply an object-based image analysis (OBIA) approach to satellite image processing and determining crop residue cover (CRC) and tillage intensity. To achieve this goal, we collected ground truth data using line-transect method from 35 plots of farmlands with an area of 528 ha. Accordingly, Landsat Operational Land Imager (OLI) satellite image together with global positioning system (GPS)-based survey data set were considered for applying the OBIA methods and deriving CRC. To process the data, object-based image processing steps including segmentation and classification were applied to develop intelligent objects and establish classification using spectral and spatial characteristics of CRC. We developed three categories of rule sets including mean indices, tillage indices, and grey-level co-occurrence matrix (GLCM) texture features using the OBIA algorithms and assign class method. Results were validated against of ground control data set and were collected by GPS in field survey. Results of this study indicated that the brightness, normalised difference tillage index, and GLCM texture feature mean performed out as effective techniques. Overall accuracy and kappa coefficient (κ) were computed to be about 0.91 and 0.86; 0.93 and 0.90; 0.60 and 0.35, respectively, for the above-mentioned indices. The foregoing discussion has attempted to demonstrate that the remotely sensed data can be effective approach and substitute for ground methods, especially in large areas.     

A random forest classifier for lymph diseases

Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.