Fuzzy Classification Rules Research Articles

Fuzzy Groundwater Classification Rule Derivation from Quality Maps

The paper presents fuzzy groundwater classification rule base derivation from groundwater quality variation maps, which are based on any three ions. These maps are obtained by geostatistical (Kriging) methodology and the results are presented in the form of equal ion concentration lines (contours). They help to visualize mutual relationships between three ions with linguistic interpretations. For this purpose each ion is separated into three linguistic categories, as ‘low,’ ‘medium’ and ‘high,’ which are characterized numerically by membership functions (MFs). MFs are transformers of linguistic expressions into numbers and vice versa. Accordingly, the linguistic fuzzy rules are presented for total dissolved solids’ (TDS) occurrence based on two different ions. In general, it is possible to deduce fuzzy logical rules relating any ion to the other two ions.

Protein Superfamily Classification Using Fuzzy Rule-Based Classifier

In this paper, we have proposed a fuzzy rule-based classifier for assigning amino acid sequences into different superfamilies of proteins. While the most popular methods for protein classification rely on sequence alignment, our approach is alignment-free and so more human readable. It accounts for the distribution of contiguous patterns of n amino acids ( n-grams) in the sequences as features, alike other alignment-independent methods. Our approach, first extracts a plenty of features from a set of training sequences, then selects only some best of them, using a proposed feature ranking method. Thereafter, using these features, a novel steady-state genetic algorithm for extracting fuzzy classification rules from data is used to generate a compact set of interpretable fuzzy rules. The generated rules are simple and human understandable. So, the biologists can utilize them, for classification purposes, or incorporate their expertise to interpret or even modify them. To evaluate the performance of our fuzzy rule-based classifier, we have compared it with the conventional nonfuzzy C4.5 algorithm, beside some other fuzzy classifiers. This comparative study is conducted through classifying the protein sequences of five superfamily classes, downloaded from a public domain database. The obtained results show that the generated fuzzy rules are more interpretable, with acceptable improvement in the classification accuracy.

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.

A Study on the Behaviour of the Algorithm for Finding Relevant Attributes and Membership Functions

A Study on the Behaviour of the Algorithm for Finding Relevant Attributes and Membership FunctionsOne of the most recent approaches in machine learning is fuzzy rules usage for solving classification problems. This paper describes the algorithm for finding relevant attributes and searching for membership functions. Experimental results are used to clarify - which data sets can be used to automatically gain primary membership functions from primary data. This quality - gaining of membership functions - is one of the pros of the algorithm, because it eases resolution of classification task. The ability to use it with fuzzy data is one more merit. As a result, there are obtained reliable fuzzy classification rules to separate classes. By reconstructing primary membership functions also the number of IF-THEN rules gained from decision tables is reduced up to three times. Four experiments are conducted with different training and testing data set sizes. Conclusions are made about the optimal size of the training and testing data set that is necessary for achieving better results as well as about the data this algorithm is appropriate for. Finally, possible directions for further research are outlined.

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

In this paper, an iterative method to select suitable features in an industrial pattern recognition context is proposed. It combines a global method of feature selection and a fuzzy linguistic rule classifier. It is applied to an industrial fabric textile context. The aim of the global vision system is to identify textile fabric defects. From the related industrial process, the training data sets are small, and some are incomplete. Moreover, the recognition step must be compatible with the time constant of the system, which generally imposes low complexity for the system. The choice of the most relevant features and the reduction of their number are important to respect these constraints. The feature selection method is based on the analysis of indexes extracted on the lattice defined from training in relation with the Choquet integral. This selection step is embedded in an iterative algorithm to discard weaker features in order to decrease the number of rules while keeping good recognition rates. The recognition step is done with a fuzzy reasoning classifier that is well adapted for this application case. The proposed method is quite efficient with small learning data sets because of the generalization capacity of both the feature selection and recognition steps. The experimental study shows the wanted behavior of this approach: the feature number decreases, whereas the recognition rate increases. Thus, the total number of generated fuzzy rules is reduced.

Formulation of Fuzzyc-Means Clustering Using Calculus of Variations and Twofold Membership Clusters

A membership matrix of fuzzyc-means clustering is associated with corresponding fuzzy classification rules as membership functions defined on the whole data space. We directly derive such functions in fuzzyc-means and possibilistic clustering using the calculus of variations, generalizing ordinary fuzzyc-means and deriving new twofold membership clustering using this framework.

SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data

This paper considers the automatic design of fuzzy-rule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this paper, we have proposed a novel steady- state genetic algorithm to extract a compact set of good fuzzy rules from numerical data (SGERD). The selection mechanism of this algorithm is nonrandom, and only the best individuals can survive. Our approach is very simple and fast, and can be applied to high-dimensional problems with numerical attributes. To select the rules having high generalization capabilities, our algorithm makes use of some rule- and data-dependent parameters. We have also proposed an enhancing function that modifies the rule evaluation measures in order to assess the candidate rules more effectively before their selection. Experiments on some well-known data sets are performed to show the performance of SGERD.

Handcrafted fuzzy rules for tissue classification

This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.

데이터 분할 평가 진화알고리즘을 이용한 효율적인 퍼지 분류규칙의 생성

데이터 속성 값이 연속적이고 애매할 때 퍼지 규칙으로 분류규칙을 표현하는 것은 매우 유용하면서도 효과적이다. 그러나 효과적인 퍼지 분류규칙을 생성하기 위한 소속함수를 결정하기는 어렵다. 본 논문에서는 진화알고리즘을 이용하여 효과적인 퍼지 분류규칙을 자동으로 생성하는 방법을 제안한다. 제안한 방법은 지도 군집화로 클래스 분포에 따라 초기 소속함수를 생성하고, 정확하고 간결한 규칙을 생성할 수 있도록 초기 소속함수를 진화시키는 방법이다. 또한 진화알고리즘의 시간에 대한 효율성을 높이기 위한 방법으로 데이터 분할 평가 진화 방법을 제안한다. 데이터 분할 평가 진화 방법은 전체 학습 데이터를 여러 개의 부분 학습 데이터들로 나누고 개체는 전체 학습 데이터 대신 부분 학습 데이터를 임의로 선택하여 평가하는 방법이다. UCI 벤치마크 데이터로 기존 방법과 비교 실험을 통해 평균적으로 제안한 방법이 효과적임을 보였다. 또한 KDD'99 Cup의 침입탐지 데이터에서 KDD'99 Cup 우승자에 비해 1.54% 향상된 인식률과 20.8% 절감된 탐지비용을 보였고 데이터 분할 평가 진화 방법으로 개체평가 시간을 약 70% 감소시켰다. Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

Designing of classifiers based on immune principles and fuzzy rules

This paper proposed an algorithm to design a fuzzy classification system based on immune principles. The proposed algorithm evolves a population of antibodies based on the clonal selection and hypermutation principles. The membership function parameters and the fuzzy rule set including the number of rules inside it are evolved at the same time. Each antibody (candidate solution) corresponds to a fuzzy classification rule set. We compared our algorithm with other classification schemes on some benchmark datasets. The results demonstrated the effectiveness of the proposed immune algorithm.

Data mining with a simulated annealing based fuzzy classification system

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the optimum set of fuzzy if–then rules. The SAFCS would be capable to extract accurate fuzzy classification rules from input data sets, and applies them to classify new data instances in different predefined groups or classes. Experiments are performed with eight UCI data sets. The results indicate that the proposed SAFCS achieves competitive results in comparison with several well-known classification algorithms.

A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining

Besides its effectiveness, supply chain management (SCM) is a complex process because of the stochastic and dynamic nature, multi-criterion and ever-increasing complexity of supply chains. Furthermore, companies have realized that agility is essential for their survival and competitiveness. Consequently, there is no generally accepted method by researchers and practitioners for designing, operating and evaluating agile supply chains. Moreover, the ability to build agile supply chain has developed more slowly than anticipated, because technology for managing agile supply chain is still being developed. Therefore, in this paper, we develop a new approach based on Fuzzy Association Rule Mining to support the decision makers by enhancing the flexibility in making decisions for evaluating agility with both tangibles and intangibles attributes/criteria such as Flexibility, Profitability, Quality, Innovativeness, Pro-activity, Speed of response, Cost and Robustness. Also, by checking the fuzzy classification rules, the goal of knowledge acquisition can be achieved in a framework in which evaluation of agility could be established without constraints, and consequently checked and compared in several details. Efficacy and intricacy of the proposed approach for finding fuzzy association rules from the database for evaluating agility is demonstrated with the help of a numerical example.

Automated predictive ecological mapping in a Forest Region of B.C., Canada, 2001–2005

Procedures for automated predictive thematic mapping were developed and applied to project areas totaling more than 3 million ha of forested land in British Columbia, Canada. The effective scale of mapping was 1:20,000 using data at a grid resolution of 25 m. The methods can be described as a form of automated feature extraction or object recognition where the objects of interest consist of ecological site types. The methods implement a hybrid of automated, semi-automated and manual procedures that develop and apply heuristic, rule-based conceptual models of ecological-landform relationships. The methods rely heavily upon terrain derivatives extracted from available digital elevation models (DEMs) in addition to satellite imagery and manually digitized maps of ancillary environmental conditions. The primary input has been the BC provincial Terrain Resource Information Management (TRIM) digital elevation model (DEM) surfaced to a regular grid of 25 m. Other input layers include manually interpreted maps of parent material texture, depth and ecological exception classes, manually prepared maps of the spatial distribution of ecological zones of the BC Biogeoclimatic Ecosystem Classification (BEC) system and, to a limited extent, LandSat7 digital satellite imagery. The procedures do not use any field sampling to develop or train classification rules. A knowledge-based approach is used to establish classification rules which are defined and implemented using a Semantic Import (SI) Model implementation of fuzzy logic. All rules are constructed by examining and deconstructing published field guides that define the required ecological output classes and that document the current expert understanding of the conditions and criteria that control the spatial distribution of these desired output classes. An iterative, trial and error, process is used to develop, apply, evaluate and revise object recognition rules that relate ranges of values of key input data layers to an expert-assigned likelihood of occurrence for each ecological class of interest. Local expert knowledge is used at each stage to evaluate each new set of output results and to guide refinement of the fuzzy SI model classification rules. Field sample observations obtained along randomly selected closed traverses were collected following a line intercept approach and used to assess the accuracy of the final predictions of ecological classification. Application of the procedures has progressed from an initial pilot project through projects to evaluate operational scale-up to full-scale commercial application to millions of hectares. Costs have been reduced from a high of $3.50/ha to less than $0.20/ha. Rates of progress increased from 150,000 ha per person year to more than 2.0 million ha per person year. Independent assessments of map accuracy produced results superior to the highest accuracies reported for all alternatives, including traditional manual mapping methods. We conclude that we have formalized and automated many of the concepts and techniques previously used to create thematic maps of ecosystems using manual interpretation of stereo air photos and ancillary data combined with field observations. We have shown that automated feature extraction is able to capture and apply the concepts of landform control referenced by typical landform-based ecological models and classification systems. We have demonstrated that it is possible to produce accurate and cost-effective ecological-landform maps by applying fuzzy and Boolean logic and automated landform analysis procedures to widely available spatial data.

Assessing daily tropical rainfall variations using a neuro-fuzzy classification model

Rainfall variations in tropical areas like Indonesia are dependent upon the tropical climate variability that has two seasons, the dry and wet seasons. However, the significant variations inherent in tropical climate are frequently affected by the combination of the atmosphere phenomena such as the El Niño-southern oscillation (ENSO) influence and tropical cyclone. This in turn lead to uncertainty of rainfall, making it difficult to develop an analysis technique that adequately assesses and interprets variations in rainfall periodicity. Many previous studies of rainfall variation have used techniques such as artificial neural networks and fuzzy methods. Each method uses a different rationale for the way in which the analysis purposes are preserved during analysis. The current study presents the use of a supervised learning of the neuro-fuzzy classification model in order to assess the rainfall variations in a tropical area. This method is a special example of a model within the field of neuro-fuzzy systems that enables the construction of the model output that could be represented by fuzzy classification rules. The classification procedures were started to derive the cluster information for the datasets by using the fuzzy C-means (FCM) clustering. Here, the process of clustering was arranged to provide two clusters of datasets, by adapting to the rainfall of dry (small rainfall) and wet (large rainfall) seasons over the study areas. Based on the prior clusters from the FCM, a neuro-fuzzy algorithm was trained to develop a set of the rule base of the classification models. The pruning strategies for the given rule base in the trained classifier were then exploited to improve the accuracy of the resulting model. The results of analysis gave strong performance by yielding a simpler rule base with a high accuracy. This enabled improved interpretability of variation in rainfall.

Supplier selection using fuzzy association rules mining approach

Owing to ill-structured, dynamic environments and the presence of multiple decision-makers with conflicting viewpoints, comprehension, analysis and support of the supplier evaluation process becomes more and more difficult. Moreover, with the complexities of issues such as the role of leadership, the influence of group formation, and analysis of disagreements, it cannot be predictable that there will ever exist a solution to cope with all imprecise, multi-criteria/multi-actor situations. A fuzzy association rules-based approach may be suited for the judgement of human subjects. In this paper, we develop an approach based on Fuzzy Association Rule Mining to support the decision makers by enhancing the flexibility in making decisions for evaluating suppliers with both tangibles and intangibles attributes. Also, by checking the fuzzy classification rules, the goal of knowledge acquisition can be achieved in a framework in which assessments could be established without constraints, and consequently checked and compared in several details. The efficacy and intricacy of the proposed model for finding fuzzy association rules from the database for supplier assessment is demonstrated with the help of numerical examples.

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

Generating both accurate as well as explanatory classification rules is becoming increasingly important in a knowledge discovery context. In this paper, we investigate the power and usefulness of fuzzy classification rules for data mining purposes. We propose two evolutionary fuzzy rule learners: an evolution strategy that generates approximate fuzzy rules, whereby each rule has its own specific definition of membership functions, and a genetic algorithm that extracts descriptive fuzzy rules, where all fuzzy rules share a common, linguistically interpretable definition of membership functions in disjunctive normal form. The performance of the evolutionary fuzzy rule learners is compared with that of Nefclass, a neurofuzzy classifier, and a selection of other well-known classification algorithms on a number of publicly available data sets and two real life Benelux financial credit scoring data sets. It is shown that the genetic fuzzy classifiers compare favourably with the other classifiers in terms of classification accuracy. Furthermore, the approximate and descriptive fuzzy rules yield about the same classification accuracy across the different data sets.

Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis

In fuzzy rule-based classification systems, rule weight has often been used to improve the classification accuracy. In past research, a number of heuristic methods for rule weight specification have been proposed. In this paper, a method of fuzzy rule weight specification using Receiver Operating Characteristic (ROC) analysis is proposed. In order to specify the weight of a fuzzy rule, using 2-class ROC analysis, the threshold that the rule achieves its maximum accuracy is found. This threshold is used as the weight of the rule. The proposed method is compared with existing ones through computer simulations on some well-known classification problems with continuous attributes. Simulation results show that the proposed method performs better than existing methods of fuzzy rule weight specification.

Fuzzy Rule–Based Approach for Detection of Bounded Weak-Echo Regions in Radar Images

Abstract A method for the detection of a bounded weak-echo region (BWER) within a storm structure that can help in the prediction of severe weather phenomena is presented. A fuzzy rule–based approach that takes care of the various uncertainties associated with a radar image containing a BWER has been adopted. The proposed technique automatically finds some interpretable (fuzzy) rules for classification of radar data related to BWER. The radar images are preprocessed to find subregions (or segments) that are suspected candidates for BWERs. Each such segment is classified into one of three possible cases: strong BWER, marginal BWER, or no BWER. In this regard, spatial properties of the data are being explored. The method has been tested on a large volume of data that are different from the training set, and the performance is found to be very satisfactory. It is also demonstrated that an interpretation of the linguistic rules extracted by the system described herein can provide important characteristics about the underlying process.

Specialization models for a general fuzzy set covering framework

Set covering is a well-known rule induction paradigm for learning crisp classification rules. In this paper we present a generalization of the crisp set covering approach to fuzzy sets. We propose a general fuzzy set covering framework for finding good rules by a general-to-specific search as a further generalization of the set covering approach to learning. The paper illustrates four different specialization model algorithms as instantiations within this framework, and concludes with an empirical study to substantiate the usefulness of this new paradigm for the induction of fuzzy classification rules.

Fuzzy rule induction in a set covering framework

Classes of algorithms and their corresponding knowledge representations for the induction of fuzzy logic classification rules include, for example, clustering and fuzzy decision trees. This paper introduces a new class of induction algorithms based on fuzzy set covering principles. We present a set covering framework for concept learning using fuzzy sets, and develop an algorithm, FUZZYBEXA, based on this approach to induce fuzzy classification rules from data. Unlike the induction of fuzzy decision trees that follow a divide-and-conquer strategy, this algorithm performs a separate-and-conquer general-to-specific search of the instance space. We show that the description language allows a partial ordering of candidate hypotheses leading to a lattice of conjunctions to be searched. Properties of the lattice allow the development of new heuristics to guide the search for good concept descriptions and to terminate the search early enough in the induction process. The operation of the algorithm is illustrated and then compared with other well-known crisp and fuzzy machine learning algorithms. The results show that highly accurate and comprehensible rules are induced, and that this methodology is an important new tool in the arsenal of fuzzy machine learning algorithms.