A dynamic rule-based classification model via granular computing

2021 ◽  
Author(s):  
Jiaojiao Niu ◽  
Degang Chen ◽  
Jinhai Li ◽  
Hui Wang
Keyword(s):  
Granular Computing ◽  
Classification Model ◽  
Rule Based

scholarly journals Improving rule-based classification using Harmony Search

2019 ◽  
Vol 5 ◽  
pp. e188 ◽  
Author(s):  
Hesam Hasanpour ◽  
Ramak Ghavamizadeh Meibodi ◽  
Keivan Navi
Keyword(s):  
Harmony Search ◽  
Search Space ◽  
Classification Model ◽  
Classification Algorithms ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Rule Based Classifier ◽  
High Processing ◽  
Fundamental Limitation

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

scholarly journals Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model

2019 ◽  
Vol 11 (10) ◽  
pp. 1193 ◽  
Author(s):  
Abdallah Shanableh ◽  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Cristina Flesia ◽  
Saeed AL-Mansoori
Keyword(s):  
Time Series ◽  
Spectral Band ◽  
Classification Model ◽  
Series Data ◽  
Natural Phenomenon ◽  
Adaptive Boosting ◽  
Rule Based ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002–2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events.

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.

A RULE-BASED SOFTWARE QUALITY CLASSIFICATION MODEL

2008 ◽  
Vol 15 (03) ◽  
pp. 247-259
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
LOFTON A. BULLARD ◽  
KEHAN GAO
Keyword(s):  
Software Quality ◽  
Rough Set Theory ◽  
Classification Model ◽  
Equal Frequency ◽  
Rule Based ◽  
Functional Aspects ◽  
Quality Classification ◽  
Proposed Model ◽  
Software Modules

A rule-based classification model is presented to identify high-risk software modules. It utilizes the power of rough set theory to reduce the number of attributes, and the equal frequency binning algorithm to partition the values of the attributes. As a result, a set of conjuncted Boolean predicates are formed. The model is inherently influenced by the practical needs of the system being modeled, thus allowing the analyst to determine which rules are to be used for classifying the fault-prone and not fault-prone modules. The proposed model also enables the analyst to control the number of rules that constitute the model. Empirical validation of the model is accomplished through a case study of a large legacy telecommunications system. The ease of rule interpretation and the transparency of the functional aspects of the model are clearly demonstrated. It is concluded that the new model is effective in achieving the software quality classification.

Multi-criteria classification of spare parts inventories – a web based approach

2015 ◽  
Vol 21 (4) ◽  
pp. 456-477 ◽  
Author(s):  
S. P. Sarmah ◽  
U. C. Moharana
Keyword(s):  
Fuzzy Rule ◽  
Spare Parts ◽  
Multiple Criteria ◽  
Classification Model ◽  
Web Based ◽  
Rule Based ◽  
Content Type ◽  
Total Cost ◽  
Rule Based Approach

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications. Originality/value – The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

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