Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems

An approach to dealing with missing data, both during the design and normal operation of a neuro-fuzzy classifier is presented in this paper. Missing values are processed within a general fuzzy min–max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes. An emphasis is put on ways of quantifying the uncertainty which missing data might have caused. This takes a form of classification procedure whose primary objective is the reduction of a number of viable alternatives rather than attempting to produce one winning class without supporting evidence. If required, the ways of selecting the most probable class among the viable alternatives found during the primary classification step, which are based on utilising the data frequency information, are also proposed. The reliability of the classification and the completeness of information is communicated by producing upper and lower classification membership values similar in essence to plausibility and belief measures to be found in the theory of evidence or possibility and necessity values to be found in the fuzzy sets theory. Similarities and differences between the proposed method and various fuzzy, neuro-fuzzy and probabilistic algorithms are also discussed. A number of simulation results for well-known data sets are provided in order to illustrate the properties and performance of the proposed approach.     

运用反例基于识别的手写数字串分割

采用基于识别的分割方法进行手写数字串分割.在识别的过程中,运用反例样本估计分类器参数,实验数据表明,这种运用反例样本训练的分类器与没有经过反例样本训练的分类器相比,将提高拒识率到19%左右,从而保证了较高的识别率,验证了只有经过反例训练的分类器的输出结果才是可信赖的.     

The Method of Virtual Experts in Mathematical Diagnostics

Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed) to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts), the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous functional is stated. Examples illustrating the proposed approach are provided. The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668.     

TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

Understanding the interactions between proteins and ligands is critical for protein function annotations and drug discovery. We report a new sequence‐based template‐free predictor (TargetATPsite) to identify the Adenosine‐5′‐triphosphate (ATP) binding sites with machine‐learning approaches. Two steps are implemented in TargetATPsite: binding residues and pockets predictions, respectively. To predict the binding residues, a novel image sparse representation technique is proposed to encode residue evolution information treated as the input features. An ensemble classifier constructed based on support vector machines (SVM) from multiple random under‐samplings is used as the prediction model, which is effective for dealing with imbalance phenomenon between the positive and negative training samples. Compared with the existing ATP‐specific sequence‐based predictors, TargetATPsite is featured by the second step of possessing the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm. Experimental results on three benchmark datasets demonstrate the efficacy of TargetATPsite. © 2013 Wiley Periodicals, Inc.     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

智能电网的10G-EPON中基于贝叶斯分类的业务感知机制

随着智能电网的发展及其多种信息业务的涌现,10G-EPON作为业务接入技术日益成为重要支撑;然而业务的多元化对10G-EPON的多业务支撑能力提出了重要挑战.为了适应电力系统中多种不同类型业务的需求,本文对智能电网的信息业务特性进行分析,提出了一种基于贝叶斯分类的10G-EPON业务感知机制;并且根据10G-EPON中OLT与ONU的主从式网络架构特点,提出了业务感知的主从式实现方式.该机制使用贝叶斯网络分析数据包的特征,进而确认待传送业务的类型.在贝叶斯业务分类的基础上,通过OLT和ONU之间的交互决定业务的资源分配和传输策略.为了验证新机制的有效性,分别从时延和丢包率两方面进行系统仿真.仿真结果表明,所提出的基于贝叶斯分类的业务感知机制在时延和丢包率具有显著的优势,能够实现业务与10G-EPON的高效匹配,提高10G-EPON在智能电网应用中多业务的区分支持能力.     

An efficient statistics-based method for the automated detection of sperm whale clicks

In this paper, we present a new method for the automated detection of sperm whale regular clicks and creaks based on statistical computations. In the first stage, a spectrogram is computed from the input waveform, followed by a noise normalisation process. A frequency domain filter is then applied, and the energy accumulated in each time frame is calculated. Two-second time-windows are then classified as containing either regular clicks, creaks, or noise based on statistical parameters using a neural network classifier. Finally, previously obtained statistical parameters are used to implement an energy-based detection criterion for the classified time-windows. Individual regular clicks and creaks are isolated by linking contiguous detected time frames. The proposed method was tested on five recordings of sperm whale sounds. Comparison of the detection performances to hand-labelled regular clicks and creaks revealed that this method outperforms two recently reported waveform-based methods when working with the same recordings files. An average percentage of detection of 86.97% was attained for the set of files. This method consumes also little computation time.     

On low order moments of the homodyned-K distribution

Fractional low order moments have been reported as beneficial for sampling computations using the K distribution. However, it has been recently pointed out that this it not the case for the homodyned-K distribution for a tissue discrimination problem. In this paper we show that such an statement is not fully justified. To that end, we follow a standard pattern recognition procedure both to determine class separability measures and to classify data with several classifiers. We conclude that the optimum order of the moments is intimately linked to the specific statistical properties of the tissues to be discriminated. Some ideas on how to choose the optimum order are discussed.     

基于激光雷达数据SVM分类的室内环境识别研究

语义地图构建对移动机器人导航与规划具有重要意义，而环境分类是语义地图构建的核心问题。目前所采用的环境分类方法匹配率较低，已成为语义地图构建所面临的主要问题。对此笔者提出了一种基于支持向量机的分类方法，该方法利用激光雷达数据提取环境几何特征，训练SVM分类器对机器人工作空间模式进行识别，并将所提算法用于室内环境的语义分类。实验结果表明，该分类方法具有较高的识别率，可有效地实现语义地图构建。     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.