A Naive Bayes approach for URL classification with supervised feature selection and rejection framework

Web page classification has become a challenging task due to the exponential growth of the World Wide Web. Uniform Resource Locator (URL)‐based web page classification systems play an important role, but high accuracy may not be achievable as URL contains minimal information. Nevertheless, URL‐based classifiers along with rejection framework can be used as a first‐level filter in a multistage classifier, and a costlier feature extraction from contents may be done in later stages. However, noisy and irrelevant features present in URL demand feature selection methods for URL classification. Therefore, we propose a supervised feature selection method by which relevant URL features are identified using statistical methods. We propose a new feature weighting method for a Naive Bayes classifier by embedding the term goodness obtained from the feature selection method. We also propose a rejection framework to the Naive Bayes classifier by using posterior probability for determining the confidence score. The proposed method is evaluated on the Open Directory Project and WebKB data sets. Experimental results show that our method can be an effective first‐level filter. McNemar tests confirm that our approach significantly improves the performance.     

基于支持向量机的隐含语意特征选择方法

隐含语意索引(LSI)是一个能有效捕获文档中词的隐含语意特征的方法。然而,用该方法选择的特征空间对文本分类来说可能不是最适合的,因为这种方法按照词的变化排序特征,而没有考虑到分类能力。支持向量机(SVM)高度的泛化能力使它特别适用于高维数据例如文档的分类。为此提出基于支持向量机的特征提取方法用于选择适于分类的LSI特征。该方法利用SVM高度泛化的分类能力, 通过使用在每一个规则下训练的分类器的参数对第k个特征对反向平方分解面的贡献w2k的值进行估计。实验表明当需要比LSI更少的训练和测试时间时,该方法能够以更为紧凑的表示方式提高分类性能。     

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     

Leaf Bagging: A novel meta heuristic optimization based framework for leaf identification

Automated plant recognition based on leaf images is a challenging task among the researchers from several fields. This task requires distinguishing features derived from leaf images for assigning class label to a leaf image. There are several methods in literature for extracting such distinguishing features. In this paper, we propose a novel automated framework for leaf identification. The proposed framework works in multiple phases i.e. pre-processing, feature extraction, classification using bagging approach. Initially, leaf images are pre-processed using image processing operations such as boundary extraction and cropping. In the feature extraction phase, popular nature inspired optimization algorithms viz. Spider Monkey Optimization (SMO), Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) have been exploited for reducing the dimensionality of features. In the last phase, a leaf image is classified by multiple classifiers and then output of these classifiers is combined using majority voting. The effectiveness of the proposed framework is established based on the experimental results obtained on three datasets i.e. Flavia, Swedish and self-collected leaf images. On all the datasets, it has been observed that the classification accuracy of the proposed method is better than the individual classifiers. Furthermore, the classification accuracy for the proposed approach is comparable to deep learning based method on the Flavia dataset.

     

Object detection using feature subset selection

Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases.     

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.     

一种基于BP和朴素贝叶斯的时间序列分类模型

针对传统时间序列分类方法需要较为繁琐的特征抽取工作以及在只有少量标记数据时分类效果不佳的问题,通过分析BP神经网络和朴素贝叶斯分类器的特点,提出一种基于BP和朴素贝叶斯的时间序列分类模型。利用了BP神经网络非线性映射能力和朴素贝叶斯分类器在少量标记数据下的分类能力,将BP神经网络抽取到的特征输入到朴素贝叶斯分类器中,可以较为有效的解决传统时间序列分类算法的问题。实验结果表明,该模型在标记数据较少的情况下的时间序列分类中具有较高的分类准确度。     

Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery

Efficiently representing and recognizing the semantic classes of the subregions of large-scale high spatial resolution (HSR) remote-sensing images are challenging and critical problems. Most of the existing scene classification methods concentrate on the feature coding approach with handcrafted low-level features or the low-level unsupervised feature learning approaches, which essentially prevent them from better recognizing the semantic categories of the scene due to their limited mid-level feature representation ability. In this article, to overcome the inadequate mid-level representation, a patch-based spatial-spectral hierarchical convolutional sparse auto-encoder (HCSAE) algorithm, based on deep learning, is proposed for HSR remote-sensing imagery scene classification. The HCSAE framework uses an unsupervised hierarchical network based on a sparse auto-encoder (SAE) model. In contrast to the single-level SAE, the HCSAE framework utilizes the significant features from the single-level algorithm in a feedforward and full connection approach to the maximum extent, which adequately represents the scene semantics in the high level of the HCSAE. To ensure robust feature learning and extraction during the SAE feature extraction procedure, a ‘dropout’ strategy is also introduced. The experimental results using the UC Merced data set with 21 classes and a Google Earth data set with 12 classes demonstrate that the proposed HCSAE framework can provide better accuracy than the traditional scene classification methods and the single-level convolutional sparse auto-encoder (CSAE) algorithm.     

Feature generation using genetic programming with application to fault classification.

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.     

PARTICLE SWARM OPTIMIZATION-BASED FEATURE SELECTION AND PARAMETER OPTIMIZATION FOR POWER SYSTEM DISTURBANCES CLASSIFICATION

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. The appropriate input features of the classifier are selected from a given set of possible features, and the structure parameters of the classifier are adapted with respect to these features and a given dataset. This paper describes the particle swarm optimization algorithm (PSO) that performs feature and model selection simultaneously for the probabilistic neural network (PNN) classifier for power system disturbances. The probabilistic neural network is one of the successful classifiers used to solve many classification problems. However, the computational effort and storage requirement of the PNN method will prohibitively increase as the number of patterns used in the training set increases. An important issue that has not been given enough attention is the selection of a “spread parameter,” also called a “smoothing parameter,” in the PNN classifier. PSO is a powerful meta-heuristic technique in the artificial intelligence field; therefore, this study proposes a PSO-based approach, called PSO-PNN, to specify the beneficial features and the value of spread parameter to enhance the performance of PNN. The experimental results indicate that the proposed PSO-based approach significantly improves the classification accuracy with the discriminating input features for PNN.     

Handwritten Chinese character recognition using fuzzy image alignment

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

基于手势特征融合的操作动作识别

针对动态复杂场景下的操作动作识别,提出一种基于手势特征融合的动作识别框架,该框架主要包含RGB视频特征提取模块、手势特征提取模块与动作分类模块。其中RGB视频特征提取模块主要使用I3D网络提取RGB视频的时间和空间特征;手势特征提取模块利用Mask R-CNN网络提取操作者手势特征;动作分类模块融合上述特征,并输入到分类器中进行分类。在EPIC-Kitchens数据集上,提出的方法识别抓取手势的准确性高达89.63%,识别综合动作的准确度达到了74.67%。     

Knowledge discovery in medical and biological datasets using a hybrid Bayes classifier/evolutionary algorithm

A key element of bioinformatics research is the extraction of meaningful information from large experimental data sets. Various approaches, including statistical and graph theoretical methods, data mining, and computational pattern recognition, have been applied to this task with varying degrees of success. Using a novel classifier based on the Bayes discriminant function, we present a hybrid algorithm that employs feature selection and extraction to isolate salient features from large medical and other biological data sets. We have previously shown that a genetic algorithm coupled with a k-nearest-neighbors classifier performs well in extracting information about protein-water binding from X-ray crystallographic protein structure data. The effectiveness of the hybrid EC-Bayes classifier is demonstrated to distinguish the features of this data set that are the most statistically relevant and to weight these features appropriately to aid in the prediction of solvation sites.     

Time series classification with feature covariance matrices

In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices into time series classification problem, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, the time index of the point and cumulative sum up to the point. Extracted feature vectors for the time instances are concatenated to construct feature matrices for the overlapping subsequences. Covariances of the feature matrices are used to describe the subsequences. Our main purpose in this work is to introduce and evaluate the feature covariance representation for time series classification. Therefore, in classification stage, firstly, 1-NN classifier is utilized. After showing the effectiveness of the representation with 1-NN classifier, the experiments are repeated with SVM classifier. The other novelty in this work is that a novel distance measure is introduced for time series by feature covariance matrix representation. Conducted experiments on UCR time series datasets show that the proposed method mostly outperforms the well-known methods such as DTW, shapelet transform and other state-of-the-art techniques.     

一种基于朴素贝叶斯的微博情感分类

本文基于二次情感特征提取算法,利用句法依存关系进行一次文本情感特征提取,在此基础上,利用情感词典,进行二次情感特征提取。构建朴素贝叶斯分类器,对采集的热门话题微博和酒店评论进行文本情感倾向性分类。主要比较了表情符号、标点符号,基于情感词典的特征提取和基于二次情感特征提取方法,在不同的组合下的分类性能,寻找更佳的微博文本情感分类预处理方法。并与酒店评论情感分类结果对比、分析,发现影响微博情感分类性能的原因。实验结果表明,二次特征提取方法在分类上取得更高的F1。实验最佳的分类预处理方式是"表情符号+标点符号+二次情感特征提取+BOOL值"。同时发现,朴素贝叶斯在酒店评论情感分类取得更高的分类性能,主要是微博评价对象多样化造成的。     

On Taxonomy and Evaluation of Feature Selection‐Based Learning Classifier System Ensemble Approaches for Data Mining Problems

Ensemble methods aim at combining multiple learning machines to improve the efficacy in a learning task in terms of prediction accuracy, scalability, and other measures. These methods have been applied to evolutionary machine learning techniques including learning classifier systems (LCSs). In this article, we first propose a conceptual framework that allows us to appropriately categorize ensemble‐based methods for fair comparison and highlights the gaps in the corresponding literature. The framework is generic and consists of three sequential stages: a pre‐gate stage concerned with data preparation; the member stage to account for the types of learning machines used to build the ensemble; and a post‐gate stage concerned with the methods to combine ensemble output. A taxonomy of LCSs‐based ensembles is then presented using this framework. The article then focuses on comparing LCS ensembles that use feature selection in the pre‐gate stage. An evaluation methodology is proposed to systematically analyze the performance of these methods. Specifically, random feature sampling and rough set feature selection‐based LCS ensemble methods are compared. Experimental results show that the rough set‐based approach performs significantly better than the random subspace method in terms of classification accuracy in problems with high numbers of irrelevant features. The performance of the two approaches are comparable in problems with high numbers of redundant features.     

Metric learning for weather image classification

Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.     

Leaf recognition based on PCNN

Plant is closely related to humans. How to quickly recognize an unknown plant without related professional knowledge is a huge challenge. With the development of image processing and pattern recognition, it is available for plant recognition based on the technique of image processing. Pulse-coupled neural network is a powerful tool for image processing. It is widely applied in the field of image segmentation, image fusion, feature extraction, etc. Support vector machine is an excellent classifier, which can finish the complex task of data exploration. Based on these two techniques, a novel plant recognition method is proposed in this paper. The key feature is the entropy sequence obtained by pulse-coupled neural network. Other ancillary features can be computed directly by mathematical and morphological methods. Both key feature and ancillary features are employed to represent the unique feature of one plant. Support vector machine in our method is taken as the classifier, which can implement the multi-class classification. Experimental results show that the proposed method can finish the task of plant recognition effectively. Compared with the existing methods, our proposed method has better recognition rate.     

Writer adaptation via deeply learned features for online Chinese handwriting recognition

This paper proposes a novel framework of writer adaptation based on deeply learned features for online handwritten Chinese character recognition. Our motivation is to further boost the state-of-the-art deep learning-based recognizer by using writer adaptation techniques. First, to perform an effective and flexible writer adaptation, we propose a tandem architecture design for the feature extraction and classification. Specifically, a deep neural network (DNN) or convolutional neural network (CNN) is adopted to extract the deeply learned features which are used to build a discriminatively trained prototype-based classifier initialized by Linde–Buzo–Gray clustering techniques. In this way, the feature extractor can fully utilize the useful information of a DNN or CNN. Meanwhile, the prototype-based classifier could be designed more compact and efficient as a practical solution. Second, the writer adaption is performed via a linear transformation of the deeply learned features which is optimized with a sample separation margin-based minimum classification error criterion. Furthermore, we improve the generalization capability of the previously proposed discriminative linear regression approach for writer adaptation by using the linear interpolation of two transformations and adaptation data perturbation. The experiments on the tasks of both the CASIA-OLHWDB benchmark and an in-house corpus with a vocabulary of 20,936 characters demonstrate the effectiveness of our proposed approach.