Comparison of clustering algorithms for analog modulation classification

This study introduces a comparative study of implementation of clustering algorithms on classification of the analog modulated communication signals. A number of key features are used for characterizing the analog modulation types. Four different clustering algorithms are used for classifying the analog signals. These most representative clustering techniques are K-means clustering, fuzzy C-means clustering, mountain clustering and subtractive clustering. Performance comparison of these clustering algorithms and the advantages and disadvantages of the methods are examined. The validity analysis is performed. The study is supported with computer simulations.     

A method for improving the accuracy of data mining classification algorithms

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted from the training process of the data mining classification algorithms, and then it assigns the classes that correspond to these rules, to the objects that must be classified. Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method.     

Comparison of different classification algorithms for underwater target discrimination

Classification of underwater targets from the acoustic backscattered signals is considered. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.     

Improving the prediction of the clinical outcome of breast cancer using evolutionary algorithms

There exist several methods for binary classification of gene expression data sets. However, in the majority of published methods, little effort has been made to minimize classifier complexity. In view of the small number of samples available in most gene expression data sets, there is a strong motivation for minimizing the number of free parameters that must be fitted to the data. In this paper, a method is introduced for evolving (using an evolutionary algorithm) simple classifiers involving a minimal subset of the available genes. The classifiers obtained by this method perform well, reaching 97% correct classification of clinical outcome on training samples from the breast cancer data set published by van't Veer, and up to 89% correct classification on validation samples from the same data set, easily outperforming previously published results.     

基于图像分类与多算法协作的目标跟踪算法

针对目标变化和背景环境的变化,提出了一种基于图像分类的多算法协作的目标跟踪算法,采用融入改进背景加权的尺度方向自适应均值漂移算法与快速压缩算法协作的方式。该算法根据图像变化原因不同将图像分为两类,图像全局变化和目标局部感兴趣区域的变化。对由光照、背景相似度和背景模糊引起的图像全局变化,采用快速压缩算法对目标进行跟踪;对由目标本身尺寸、旋转和遮挡引起的目标局部感兴趣区域变化,采用融入改进背景加权尺度方向自适应均值漂移算法对目标进行跟踪。该算法先对图像序列预处理分类,然后选择适合该对应图像变化特点的算法对目标进行跟踪。经实验验证,该算法较之其他流行目标跟踪算法具有更好的鲁棒性。     

基于国际头痛诊断标准的原发性头痛辅助决策系统

针对原发性头痛在我国发病率较高但诊断准确率较低的问题,提出了一种将文本化的国际头痛诊断标准转换为计算机可执行推理的临床知识建模和知识库构建方法,该方法首先将基于诊断标准绘制的诊断思维流程图转换为规范化的临床知识表达模型,再将临床知识表达模型通过规则映射技术转换为计算机推理诊断所用的规则,形成知识库。并以此为基础开发了覆盖完整头痛诊断流程的原发性头痛辅助决策系统。临床评估显示,该系统可正确识别出91.3%的偏头痛、87.2%的紧张型头痛和90.0%的丛集性头痛病人,对常见的原发性头痛具有较高的诊断准确率。     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

Learning algorithms in a class of knowledge-based systems

A control system with a static plant described by knowledge representation in the forms of relations and of logical formulas is considered. The learning process consists in using successive knowledge validation and updating it to make current control decisions. Two approaches and algorithms are described: one for the validation and updating of knowledge about the plant, and one on the form of control. In both cases two versions are presented: the learning process in either an open-loop or a closed-loop control system. For a plant with logical knowledge representation, the logic-algebraic method is applied. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Probabilistic design of optimal sequential decision-making algorithms in learning and control

This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a problem formulation and a set of resolution methods. The formulation consists of an infinite-dimensional optimization problem. The methods come from approaches to search optimal solutions in the space of probability functions. Through the lenses of this overarching framework we revisit popular learning and control algorithms, showing that these naturally arise from suitable variations on the formulation mixed with different resolution methods. A running example, for which we make the code available, complements the survey. Finally, a number of challenges arising from the survey are also outlined.     

Ensemble of feature sets and classification algorithms for sentiment classification

In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely na?¨ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.     

Hybrid genetic algorithms and case-based reasoning systems for customer classification

Abstract: Because of its convenience and strength in complex problem solving, case-based reasoning (CBR) has been widely used in various areas. One of these areas is customer classification, which classifies customers into either purchasing or non-purchasing groups. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most previous studies have tried to optimize the weights of the features or the selection process of appropriate instances. But these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than naive models. In particular, there have been few attempts to simultaneously optimize the weights of the features and the selection of instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm. To validate the usefulness of our approach, we apply it to two real-world cases for customer classification. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.     

hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.     

企业核心竞争力的动态不确定多尺度决策研究

针对传统企业核心竞争力的评价以静态、定性为主的缺陷,从动态不确定的角度,通过引入偏差变量的方法建立不确定性偏好排序的统一模型,提出了综合考虑区间数关联度和方案序的决策者权重方法.构建企业核心竞争力动态不确定多尺度评价模型,并进行了算例实证.结果表明该方法可以对企业核心竞争力进行动态和定量的评价,更具有科学性.     

Approximation algorithms in partitioning real-time tasks with replications

Today is an era where multiprocessor technology plays a major role in designs of modern computer architecture. While multiprocessor systems offer extra computing power, it also opens a new range of opportunities to improve fault-robustness. This paper focuses on a problem of achieving fault-tolerance using replications in real-time, multiprocessor systems. In the problem, multiple replicas, or copies, of a computing task are executed on distinct processors to resist potential processor failures and computing faults. Two greedy, approximation heuristics, named Worst Fit Increasing K-Replication and First Fit Increasing K-Replication, are studied to maximise the number of real-time tasks assigned on a system with identical processors, respecting to the tasks’ replicating and timely requirements. Worst case performance is analysed by using an approximation ratio between the algorithms and an optimal solution. We mathematically prove that the ratios of using both algorithms are infinitely close to 2. Simulations are performed on a large set of testing cases which can be used to bring to light the average performance of using the algorithms in practice. The results show that both heuristic algorithms provide simple but fast and effective solutions to solve the problem.     

Benchmarking binary classification models on data sets with different degrees of imbalance

In practice, there are many binary classification problems, such as credit risk assessment, medical testing for determining if a patient has a certain disease or not, etc. However, different problems have different characteristics that may lead to different difficulties of the problem. One important characteristic is the degree of imbalance of two classes in data sets. For data sets with different degrees of imbalance, are the commonly used binary classification methods still feasible? In this study, various binary classification models, including traditional statistical methods and newly emerged methods from artificial intelligence, such as linear regression, discriminant analysis, decision tree, neural network, support vector machines, etc., are reviewed, and their performance in terms of the measure of classification accuracy and area under Receiver Operating Characteristic (ROC) curve are tested and compared on fourteen data sets with different imbalance degrees. The results help to select the appropriate methods for problems with different degrees of imbalance.     

A genetic fuzzy expert system for automatic question classification in a competitive learning environment

Intelligent tutoring systems are efficient tools to automatically adapt the learning process to the student’s progress and needs. One of the possible adaptations is to apply an adaptive question sequencing system, which matches the difficulty of the questions to the student’s knowledge level. In this context, it is important to correctly classify the questions to be presented to students according to their difficulty level. Many systems have been developed for estimating the difficulty of questions. However the variety in the application environments makes difficult to apply the existing solutions directly to other applications. Therefore, a specific solution has been designed in order to determine the difficulty level of open questions in an automatic and objective way. This solution can be applied to activities with special temporal and running features, as the contests developed through QUESTOURnament, which is a tool integrated into the e-learning platform Moodle. The proposed solution is a fuzzy expert system that uses a genetic algorithm in order to characterize each difficulty level. From the output of the algorithm, it defines the fuzzy rules that are used to classify the questions. Data registered from a competitive activity in a Telecommunications Engineering course have been used in order to validate the system against a group of experts. Results show that the system performs successfully. Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.     

Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning

Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users.The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem.We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.     

中医临床不均衡数据疾病分类方法研究

基于欠采样的不均衡数据分类算法是一种随机数据优化算法,但它不能最好地反映中医临床原始数据的分布并解决数据的特征冗余问题。提出了基于预测风险的最远病例不均衡装袋算法（PRFS-FPUSAB）。该算法中首先基于欠采样提出了改进的抽样方式尽可能地反映原始数据分布,然后结合集成学习、预测风险标准提高不均衡的分类性能并进行特征选择。在中医临床采集的经络电阻数据上的实验结果表明,该算法改善了曲线下面积并且选择的特征也符合中医学相关理论。     

Mapping algorithms in ispahan

A number of mapping operations in pattern recognition is reviewed. Two families of new mapping algorithms are defined. The performance of all algorithms is illustrated using a collection of 100 feature vectors obtained from images of two classes of white blood cells. It is emphasized that the interactive pattern recognition system ISPAHAN is well suited to find optimal decision functions based on such mappings.