Breast cancer diagnosis using genetic programming generated feature

This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimize features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.     

Dimensionality reduction using genetic algorithms

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces     

Application of genetic programming for multicategory patternclassification

Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them mathematically. GP-based techniques have an advantage over statistical methods because they are distribution-free, i.e., no prior knowledge is needed about the statistical distribution of the data. GP also automatically discovers the discriminant features for a class. GP has been applied for two-category classification. A methodology for GP-based n-class classification is developed. The problem is modeled as n two-class problems, and a genetic programming classifier expression (GPCE) is evolved as a discriminant function for each class. The GPCE is trained to recognize samples belonging to its own class and reject others. A strength of association (SA) measure is computed for each GPCE to indicate the degree to which it can recognize samples of its own class. SA is used for uniquely assigning a class to an input feature vector. Heuristic rules are used to prevent a GPCE with a higher SA from swamping one with a lower SA. Experimental results are presented to demonstrate the applicability of GP for multicategory classification, and they are found to be satisfactory. We also discuss the various issues that arise in our approach to GP-based classification, such as the creation of training sets, the role of incremental learning, and the choice of function set in the evolution of GPCE, as well as conflict resolution for uniquely assigning a class     

Evolutionary constructive induction

Feature construction in classification is a preprocessing step in which one or more new attributes are constructed from the original attribute set, the object being to construct features that are more predictive than the original feature set. Genetic programming allows the construction of nonlinear combinations of the original features. We present a comprehensive analysis of genetic programming (GP) used for feature construction, in which four different fitness functions are used by the GP and four different classification techniques are subsequently used to build the classifier. Comparisons are made of the error rates and the size and complexity of the resulting trees. We also compare the overall performance of GP in feature construction with that of GP used directly to evolve a decision tree classifier, with the former proving to be a more effective use of the evolutionary paradigm.     

基于遗传编程的SMT机器视觉检测特征提取

在印刷电路板贴片安装的机器视觉检测中,贴片元件的型号识别和缺陷检测都是基于CCD采集的图像,数据量大、维数太多,该图像特征的提取是正确检测识别的关键技术之一.遗传编程通过遗传优化可以从原始数据或传统的高维特征中提取出更能反映类别本质的有效特征,降低特征维数、减少分类器的计算成本,同时提高分类识别精度.设计基于遗传编程的特征提取方案用于该机器视觉检测,并改进了特征评价指标.对比实验验证了本方案提取的特征分别用于ANN和SVM的良好分类识别效果.     

基于小波分解和优选的VLBP特征的表情识别方法*

提出一种基于小波分解和优选的VLBP特征的表情识别方法。该方法首先通过小波分解将原始图像分为几个不同频率的子图像来增强图像信息,然后用VLBP算子对不同频率的子图像运用不同的分块大小提取特征,采用神经网络贡献分析对特征进行选择,最后用SVM分类器进行识别。实验表明,该方法比单纯从原图像中提取VLBP特征更加有效,识别率更高,并且VLBP特征的提取速度快,可用于实时的人脸表情识别。     

Parallel supervised land-cover classification system for hyperspectral and multispectral images

Hyperspectral and multispectral imagery allows remote-sensing applications such as the land-cover mapping, which is a significant baseline to understand and to monitor the Earth. Furthermore, it is a relevant process for socio-economic activities. For that reason, high land-classification accuracies are imperative, and minor image processing time is essential. In addition, the process of gathering classes’ documented samples is complicated. This implies that the classification system is required to perform with a limited number of training observations. Another point worth mentioning is that there are hardly any methods that can be used analogously for hyperspectral or multispectral images. This paper aims to propose a novel classification system that can be used for both types of images. The designed classification system is composed of a novel parallel feature extraction algorithm, which utilises a cluster of two graphics processing units in combination with a multicore central processing unit (CPU), and an artificial neural network (ANN) particularly devised for the classification of the features ensued by the implemented feature extraction method. To prove the performance of the proposed classification system, it is compared with non-parallel and CPU-only-parallel implementations employing multispectral and hyperspectral databases. Moreover, experiments with different number of samples for training the classifier are performed. Finally, the proposed ANN is compared with a state-of-the-art support vector machine in classification and processing time results.     

基于多维特征及BP网络的高分辨雷达目标识别

高分辨雷达目标的识别性能取决于目标特征的提取以及分类器的设计。为解决雷达高分辨距离像（HRRP）的方位、平移和幅度敏感性问题,采用了序贯预处理方法,有效提高了HRRP的信噪比。通过提取能较好反映雷达目标散射点回波特性的多维特征向量,设计BP神经网络作为分类器,提出了一种基于目标多维特征向量以及BP神经网络的高分辨雷达目标识别方法。利用在微波暗室测量获得的三种国产飞机模型回波数据进行目标识别处理,实验结果表明,提出的方法能有效地完成三种目标识别任务,在虚警率低于3%的情况下正确识别率优于95%。     

Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning

Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has three main phases: feature extraction, feature selection and feature classification. Histogram features can be used for fault diagnosis of roller bearing. This paper presents the use of decision tree for selecting best few histogram features (bin ranges) that will discriminate the fault conditions of the bearing from given train samples. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezoelectric transducer is captured for the following conditions – good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The histogram features were extracted and good features that discriminate the different fault conditions of the bearing were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.     

基于自注意力机制辅助分类生成对抗网络的轴承故障诊断

针对传统判别式轴承故障诊断算法在复杂工况下依赖人工特征提取、诊断效果不佳的问题,提出将生成式模型辅助分类生成对抗网络(auxiliary classifier generative adversarial network,ACGAN)用于轴承故障诊断研究。首先,通过快速傅里叶变换将轴承振动信号转为2维频域特征灰度图,设计卷积网络作为模型主体结构,添加批量归一化和LeakyReLU激活函数缓解梯度消失问题;其次,引入自注意力机制(self-attention mechanism,SA),将数据中相距较远的特征相互关联建立新的SA-ACGAN模型,实现多分类场景下对原始数据分布特征的有效学习;最后,将模型应用于电机轴承进行对比验证,结果表明所提方法故障诊断准确率高达99.7％,并具有良好的鲁棒性和泛化性。     

American Sign Language word recognition with a sensory glove using artificial neural networks

An American Sign Language (ASL) recognition system is being developed using artificial neural networks (ANNs) to translate ASL words into English. The system uses a sensory glove called the Cyberglove™ and a Flock of Birds^® 3-D motion tracker to extract the gesture features. The data regarding finger joint angles obtained from strain gauges in the sensory glove define the hand shape, while the data from the tracker describe the trajectory of hand movements. The data from these devices are processed by a velocity network with noise reduction and feature extraction and by a word recognition network. Some global and local features are extracted for each ASL word. A neural network is used as a classifier of this feature vector. Our goal is to continuously recognize ASL signs using these devices in real time. We trained and tested the ANN model for 50 ASL words with a different number of samples for every word. The test results show that our feature vector extraction method and neural networks can be used successfully for isolated word recognition. This system is flexible and open for future extension.     

一种神经网络文本分类器的设计与实现

论文着重介绍了一种基于神经网络的文本分类器,分类器使用神经网络作为分类工具,特征词的词频组成原始特征向量,和神经网络输入层的神经元一一对应。并引入了信息检索中的常用技术——潜在语义索引,训练过程中结合遗传算法,优化神经网络的初始权值。最后对分类器进行了开放性测试,实验表明分类器对文本分类具有较高的平均查全率和平均精度。     

Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem. Using IKONOS high spatial resolution imagery, we examined whether the IO technique could significantly improve classification accuracy compared to the pixel-based method when applied to urban land cover mapping in Tampa Bay, FL, USA. We further compared the performance of an artificial neural network (ANN) and a minimum distance classifier (MDC) in urban detailed land cover classification and evaluated whether the classification accuracy was affected by the number of extracted IO features. Our analysis methods included IKONOS image data calibration, data fusion with the pansharpening (PS) process, Hue–Intensity–Saturation (HIS) transferred indices and textural feature extraction, and feature selection using a stepwise discriminant analysis (SDA). The classification results were evaluated with visually interpreted data from high-resolution (0.3 m) digital aerial photographs. Our results indicate a statistically significant difference in classification accuracy between pixel- and object-based techniques; ANN outperforms MDC as an object-based classifier; and the use of more features (27 vs. 9 features) increases the IO classification accuracy, although the increase is statistically significant for the MDC but not for the ANN.     

Automatic Feature Extraction for Classifying Audio Data

Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew.This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.     

奇异值分解和稀疏自编码器的轴承故障诊断

针对滚动轴承故障特征提取和分类需要进行有监督训练才能实现等问题,提出了一种基于奇异值分解（SVD）和时域统计特征分析并结合堆栈稀疏自编码器（SAE）以及Softmax分类器实现滚动轴承故障诊断方法。该方法利用Hankle矩阵对原始数据进行矩阵重构,利用奇异值分解和时域分析对重构后的故障信号进行特征预提取,融合两种特征并输入到堆栈稀疏自编码器中进行特征优化,将优化后的特征输入到Softmax分类器中进行分类识别。实验结果表明,3种工况下10类故障数据的识别准确率均在96%左右,且高于文中其他方法,因此该方法能有效地进行滚动轴承复杂信号的特征预处理以及分类。     

Two-Tier genetic programming: towards raw pixel-based image classification

Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.     

基于改进的CBOW与ABiGRU的文本分类研究

文本的表示与文本的特征提取是文本分类需要解决的核心问题,基于此,提出了基于改进的连续词袋模型（CBOW）与ABiGRU的文本分类模型。该分类模型把改进的CBOW模型所训练的词向量作为词嵌入层,然后经过卷积神经网络的卷积层和池化层,以及结合了注意力（Attention）机制的双向门限循环单元（BiGRU）神经网络充分提取了文本的特征。将文本特征向量输入到softmax分类器进行分类。在三个语料集中进行的文本分类实验结果表明,相较于其他文本分类算法,提出的方法有更优越的性能。     

Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection

A study is presented to compare the performance of bearing fault detection using two different classifiers, namely, artificial neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to the classifiers for two-class (normal or fault) recognition. The classifier parameters, e.g., the number of nodes in the hidden layer in case of ANNs and the radial basis function kernel parameter (width) in case of SVMs along with the selection of input features are optimized using genetic algorithms. The classifiers are trained with a subset of the experimental data for known machine conditions and are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and signal preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Fingerprint classification using a feedback-based line detector

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop, Right loop, Whorl, and Tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector presented here. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.     

Feature generation using genetic programming with comparative partner selection for diabetes classification

The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov–Smirnov test, Kullback–Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), utilising the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbor and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.