GENERATING FUZZY RULES FROM TRAINING DATA CONTAINING NOISE FOR HANDLING CLASSIFICATION PROBLEMS

It is obvious that one of the important tasks in a fuzzy system is to find a set of rules to deal with a specific classification problem. In recent years, many researchers focused on the research topic of generating fuzzy rules from training data for handling classification problems. In a previous paper, we presented an algorithm to construct membership functions and to generate fuzzy rules from training examples. In this paper, we extend that work to propose a new algorithm to generate fuzzy rules from training data containing noise to deal with classification problems. The proposed algorithm gets a higher classification accuracy rate and generates fewer fuzzy rules and fewer input attributes in the antecedent portions of the generated fuzzy rules.     

AUTOMATICALLY CONSTRUCTING MEMBERSHIP FUNCTIONS AND GENERATING FUZZY RULES USING GENETIC ALGORITHMS

The most important task in designing a fuzzy classification system is to find a set of fuzzy rules from training data to deal with a specific classification problem. In recent years, many methods have been proposed to construct membership functions and generate fuzzy rules from training data for handling fuzzy classification problems. We propose a new method to generate fuzzy rules from training data by using genetic algorithms (GAs). First, we divide the training data into several clusters by using the weighted distance clustering method and generate a fuzzy rule for each cluster. Then, we use GAs to tune the membership functions of the generated fuzzy rules. The proposed method attains a higher average classification accuracy rate than the existing methods.     

A NEW METHOD TO GENERATE FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

Fuzzy decision trees can be used to generate fuzzy rules from training instances to deal with forecasting and classification problems. We propose a new method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated fuzzy rules. We use the concept of "coefficient of determination" of the statistics to derive the weights of the attributes in relational database systems and use the normalized weights of the attributes to derive the values of certainty factors of the generated fuzzy rules. Furthermore, we also use regression equations of the statistics to construct a complete fuzzy decision tree for generating better fuzzy rules. The proposed method obtains a higher average estimated accuracy rate than the existing methods for estimating null values in relational database systems.     

A NEW METHOD FOR CONSTRUCTING FUZZY DECISION TREES AND GENERATING FUZZY CLASSIFICATION RULES FROM TRAINING EXAMPLES

This paper presents a new method for constructing fuzzy decision trees and generating fuzzy classification rules from training instances using compound analysis techniques. The proposed method can generate simpler fuzzy classification rules and has a better classification accuracy rate than the existing method. Furthermore, the proposed method generated less fuzzy classification rules.     

A new method for constructing membership functions and fuzzy rulesfrom training examples

To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.     

Generating fuzzy rules from training instances for fuzzy classification systems

In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris data classification problem based on the attribute threshold value α, the classification threshold value β and the level threshold value γ, where α  [0, 1], β  [0, 1] and γ  [0, 1]. The proposed method gets a higher average classification accuracy rate than the existing methods.     

Design of adaptive fuzzy model for classification problem

The main theme of this paper is to set up an adaptive fuzzy model for a new classification problem. At first, we propose a fuzzy classification model that can automatically generate the fuzzy IF-THEN rules by the features of the training database. The consequent part of the fuzzy IF-THEN rule consists of the confident value of the rule and which class the datum should belong to. Then a novel adaptive modification algorithm (AMA) is developed to tune the confident value of the fuzzy classification model. The proposed model comprises three modules, generation of the fuzzy IF-THEN rules, determination of the classification unit, and setup of the AMA. Computer simulations on the well known Wine and Iris databases have tested the performance. Simulations demonstrate that the proposed method can provide sufficiently high classification rate in comparison with other fuzzy classification models.     

Design of hierarchical fuzzy model for classification problem using GAs

This paper proposes a new hierarchical fuzzy model (HFM) to solve the classification problem. The developed classification model comprises of two stages; one is to generate the fuzzy IF–THEN rules for each subsystem and the other is to determine the classification unit. For the classification problem, number of rules and the correct classification rate are the fundamental requirements. In this paper, we also advance two genetic algorithms (GAs) to tune the HFM. One is used to determine the combination of the input features for each subsystem on the HFM and the other is to reduce the number of rules in each fuzzy subsystem. The performance has been tested by simulations on the well known Wine and Iris databases. Simulations demonstrate that the proposed HFM under a few rules can provide sufficiently high classification rate even with higher feature dimensions.     

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.     

Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.     

Elicitation of classification rules by fuzzy data mining

Data mining techniques can be used to find potentially useful patterns from data and to ease the knowledge acquisition bottleneck in building prototype rule-based systems. Based on the partition methods presented in simple-fuzzy-partition-based method (SFPBM) proposed by Hu et al. (Comput. Ind. Eng. 43(4) (2002) 735), the aim of this paper is to propose a new fuzzy data mining technique consisting of two phases to find fuzzy if–then rules for classification problems: one to find frequent fuzzy grids by using a pre-specified simple fuzzy partition method to divide each quantitative attribute, and the other to generate fuzzy classification rules from frequent fuzzy grids. To improve the classification performance of the proposed method, we specially incorporate adaptive rules proposed by Nozaki et al. (IEEE Trans. Fuzzy Syst. 4(3) (1996) 238) into our methods to adjust the confidence of each classification rule. For classification generalization ability, the simulation results from the iris data demonstrate that the proposed method may effectively derive fuzzy classification rules from training samples.     

一种复杂模糊系统生成方法

生成模糊系统传统方法的工作量往往随输入变量数的增长而爆炸性也增加，用于抽取模糊规则的神经网络的规模迅速地增加且能量的极小值点也迅速地增多。针对这一问题，本文发展了一种新的模糊系统生成方法，将复杂系统的模糊输入，输出关系分解成简单的模糊输入，输出关系叠加，采用了一种新的网络优化的方法－基于浮点编码的遗传算法来生成该系统。     

A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.     

基于改进关联分类的两次学习方法

关联分类通常产生大量的分类规则,导致在分类新实例时经常产生规则冲突问题。针对这种规则冲突问题,提出了一种基于改进关联分类的两次学习框架。利用频繁且互关联的项集产生分类规则改进关联分类算法,有效减少了规则数。应用改进的关联分类算法产生的一级规则一次性分离出训练集中规则冲突的所有实例。然后,在冲突实例上应用改进的关联分类算法进行第二次学习得到二级规则。分类新实例时,首先利用第一级规则进行分类。如果出现规则冲突,则利用第二级规则分类该实例。实验结果表明,基于改进关联分类的两次学习方法降低了规则冲突比率,并且显著提高了分类准确率。     

基于类权重的模糊不平衡数据分类方法

针对现有分类算法通常对不平衡数据挖掘表现出有偏性,即正类样本（通常是更重要的一类）的分类和预测性能差于负类样本的分类和预测性能,提出一种不平衡数据分类方法。该方法通过一个超球面将两类数据以最大分离比率分离,并且引入类权重因子和样本模糊隶属度,同时考虑了不同类的重要性和不同样本对该类的不同贡献,从而提高了不平衡数据中正类的分类和预测的性能以及整体的推广能力。分别在人造数据和UCI真实数据上进行了实验,结果验证了该方法的有效性。     

基于Boosting算法集成遗传模糊分类器的文本分类

提出一种新颖的基于Boosting模糊分类的文本分类方法。首先采用潜在语义索引(LSI)对文本特征进行选择;然后提出Boosting算法集成模糊分类器学习,在每轮迭代训练过程中,算法通过调整训练样本的分布,利用遗传算法产生分类规则。减少分类规则能够正确分类样本的权值,使得新产生的分类规则重点考虑难于分类的样本。实验结果表明,该文本分类算法具有良好分类的性能。     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.     

关联规则对监控下行人属性识别影响的研究

针对监控视频下的行人多属性识别问题,提出一种结合神经网络与关联规则的多分类方法。首先通过Faster-RCNN检测算法与改进的AlexNet多分类网络得到监控视频下行人各个属性的置信度,再采用关联规则Apriori算法对训练数据进行处理,进而结合神经网络分类的置信度和关联规则的处理结果,提出一种对分类置信度进行优化的算法。最后,统计关联规则优化后的某些行人属性准确率。结果表明,将神经网络与关联规则有效结合后可以提升某些属性识别的准确率。     

An adaptive rule-based classifier for mining big biological data

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.