scholarly journals Compact Belief Rule Base Learning for Classification with Evidential Clustering

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 443 ◽  
Author(s):  
Lianmeng Jiao ◽  
Xiaojiao Geng ◽  
Quan Pan
Keyword(s):  
Classification System ◽  
Optimization Procedure ◽  
Classification Model ◽  
Rule Base ◽  
Data Sets ◽  
Classification Problems ◽  
Ecm Algorithm ◽  
Rule Based ◽  
Belief Rule Base ◽  
Belief Function Theory

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal.

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

scholarly journals Regularization Method for Rule Reduction in Belief Rule-based SystemRegularization Method for Rule Reduction in Belief Rule-based System

2020 ◽  
Author(s):  
Yu Guan
Keyword(s):  
Reduction Method ◽  
Rule Base ◽  
Activation Process ◽  
Training Method ◽  
Fuzzy Information ◽  
Data Set ◽  
Inference System ◽  
Rule Based ◽  
Rule Based System ◽  
Belief Rule Base

Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.

scholarly journals Data Cleaning for Classification Using Misclassification Analysis

2010 ◽  
Vol 14 (3) ◽  
pp. 297-302 ◽  
Author(s):  
Piyasak Jeatrakul ◽  
◽  
Kok Wai Wong ◽  
Chun Che Fung
Keyword(s):  
Data Cleaning ◽  
Binary Classification ◽  
Good Alternative ◽  
Training Data ◽  
Classification Model ◽  
Data Sets ◽  
Pima Indians ◽  
Classification Problems ◽  
Data Set ◽  
Preprocessing Technique

In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could sometime affect the classification accuracies of a model. In this paper, we investigate the use of misclassification analysis for data cleaning. In order to demonstrate our concept, we have used Artificial Neural Network (ANN) as the core computational intelligence technique. We use four benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository to investigate the results from our proposed data cleaning technique. The experimental data sets used in our experiment are binary classification problems, which are German credit data, BUPA liver disorders, Johns Hopkins Ionosphere and Pima Indians Diabetes. The results show that the proposed cleaning technique could be a good alternative to provide some confidence when constructing a classification model.

Predicting Job Performance with a Fuzzy Rule-Based System

2003 ◽  
Vol 02 (03) ◽  
pp. 425-444 ◽  
Author(s):  
J. Philip Craiger ◽  
Michael D. Coovert ◽  
Mark S. Teachout
Keyword(s):  
Job Performance ◽  
Fuzzy Rule ◽  
Data Sets ◽  
Behavioral Models ◽  
Classification Problems ◽  
Rule Based ◽  
Job Experience ◽  
Rule Based System ◽  
Us Air Force ◽  
And Performance

Classification problems affect all organizations. Important decisions affecting an organization's effectiveness include predicting the success of job applicants and the matching and assignment of individuals from a pool of applicants to available positions. In these situations, linear mathematical models are employed to optimize the allocation of an organization's human resources.Use of linear techniques may be problematic, however, when relationships between predictor and criterion are nonlinear. As an alternative, we developed a fuzzy associative memory (FAM: a rule-based system based on fuzzy sets and logic) and used it to derive predictive (classification) equations composed of measures of job experience and job performance. The data consisted of two job experience factors used to predict measures of job performance for four US Air Force job families. The results indicated a nonlinear relationship between experience and performance for three of the four data sets. The overall classification accuracy was similar for the two systems, although the FAM provided better classification for two of the jobs. We discuss the apparent nonlinear relationships between experience and performance, and the advantages and implications of using these systems to develop and describe behavioral models.

scholarly journals Fuzzy C-Means com Método Wrapper Com Baixo Custo Computacional de Seleção de Atributos

2021 ◽  
Author(s):  
Gabriel Marcondes Santos ◽  
Emmanuel Tavares Ferreira Affonso ◽  
Alisson Marques Silva ◽  
Gray Farias Moita
Keyword(s):  
Computational Cost ◽  
Attribute Selection ◽  
Classification Model ◽  
Classification Algorithms ◽  
Data Sets ◽  
Features Selection ◽  
Classification Problems ◽  
Original Algorithm ◽  
Fuzzy C Means ◽  
Irrelevant Attributes

Nowadays the Computational Intelligence (IC) algorithms have shown a lot of efficiency in pattern classification and recognition processes. However, some databases may contain irrelevant attributes that may be detrimental to the learning of the classification model. In order to detect and exclude input attributes with little representativeness in the data sets presented to the classification algorithms, the Features Selection (FS) methods are commonly used. The goal of features selection methods is to minimize the number of input attributes processed by a classifier in order to improve its assertiveness. In this way, this work aims to analyze solutions to classification problems with three different classification algorithms. The first approach used for classification is the unsupervised Fuzzy C-Means (FCM) algorithm, the second approach is a supervised version of FCM and the third approach is a variation of supervised FCM with features selection. The method of features selection incorporated in FCM is called the Mean Ratio Feature Selection (MRFS), and was developed with the objective of being a method with low computational cost, without need for complex mathematical equations and can be easily incorporated into any classifier. For the experiments, the three versions of the unsupervised FCM, supervised FCM and FCM with attribute selection were performed with the aim of verifying whether there would be a significant improvement between the variations of the FCM. The results of the experiments showed that FCM with MRFS is promising, with results superior to the original algorithm and also to its supervised version.

scholarly journals Selective ensemble learning method for belief-rule-base classification system based on PAES

2019 ◽  
Vol 2 (4) ◽  
pp. 306-318 ◽  
Author(s):  
Wanling Liu ◽  
Weikun Wu ◽  
Yingming Wang ◽  
Yanggeng Fu ◽  
Yanqing Lin
Keyword(s):  
Ensemble Learning ◽  
Classification System ◽  
Rule Base ◽  
Learning Method ◽  
Belief Rule Base ◽  
Selective Ensemble

A Clustering Rule Based Approach for Classification Problems

2012 ◽  
Vol 8 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Philicity K. Williams ◽  
Caio V. Soares ◽  
Juan E. Gilbert
Keyword(s):  
Missing Data ◽  
Test Data ◽  
Missing Values ◽  
Training Data ◽  
Rule Base ◽  
Classification Problems ◽  
Rule Based ◽  
Rule Sets ◽  
The Individual ◽  
Rule Based Approach

Predictive models, such as rule based classifiers, often have difficulty with incomplete data (e.g., erroneous/missing values). So, this work presents a technique used to reduce the severity of the effects of missing data on the performance of rule base classifiers using divisive data clustering. The Clustering Rule based Approach (CRA) clusters the original training data and builds a separate rule based model on the cluster wise data. The individual models are combined into a larger model and evaluated against test data. The effects of the missing attribute information for ordered and unordered rule sets is evaluated and the collective model (CRA) is experimentally used to show that its performance is less affected than the traditional model when the test data has missing attribute values, thus making it more resilient and robust to missing data.

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