Develop Multi-hierarchy Classification Model: Rough Set Based Feature Decomposition Method

2005 ◽  
pp. 164-171 ◽  
Author(s):  
Qingdong Wang ◽  
Huaping Dai ◽  
Youxian Sun
Keyword(s):  
Rough Set ◽  
Decomposition Method ◽  
Classification Model

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals A Method to Make Classification of the Heat Treatment Processes Performed on Bronze Using Incomplete Knowledge

2014 ◽  
Vol 14 (2) ◽  
pp. 69-72 ◽  
Author(s):  
S. Kluska-Nawarecka ◽  
Z. Górny ◽  
K. Regulski ◽  
D. Wilk-Kołodziejczyk ◽  
Z. Jančíková ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Experimental Studies ◽  
Classification Model ◽  
Treatment Processes ◽  
Heat Treated ◽  
Measurement Results ◽  
Full Knowledge

Abstract The article describes the problem of selection of heat treatment parameters to obtain the required mechanical properties in heat- treated bronzes. A methodology for the construction of a classification model based on rough set theory is presented. A model of this type allows the construction of inference rules also in the case when our knowledge of the existing phenomena is incomplete, and this is situation commonly encountered when new materials enter the market. In the case of new test materials, such as the grade of bronze described in this article, we still lack full knowledge and the choice of heat treatment parameters is based on a fragmentary knowledge resulting from experimental studies. The measurement results can be useful in building of a model, this model, however, cannot be deterministic, but can only approximate the stochastic nature of phenomena. The use of rough set theory allows for efficient inference also in areas that are not yet fully explored.

scholarly journals An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

2021 ◽  
Vol 5 (3) ◽  
pp. 92-108
Author(s):  
Rana Aamir Raza
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Set Theory ◽  
Rough Set ◽  
Large Scale ◽  
Rough Set Theory ◽  
Classification Model ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Random Weight

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).

The Research of Data Stream Classification Based on Rough Set Theory-Neural Network Integration

2013 ◽  
Vol 441 ◽  
pp. 717-720
Author(s):  
Zhi Bo Ren ◽  
Chun Miao Yan ◽  
Yu Zhou Wei ◽  
Lei Sun
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Data Stream ◽  
High Speed ◽  
Rough Set Theory ◽  
Classification Model ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Voting Rule

According to the high speed of data arriving, a large amount of data and concept drifting in the stream model, combining the techniques of rough set theory, neural network and voting rule, we put forward a new data stream classification model, which is a multi-classifier integration based on rough set theory, neural network. Firstly, it reduces all attributes using rough set theory; secondly, it constructs base classifiers on the data chunks after the reduction of attributes using the improved BP neural network; finally, it fuses various base classifiers into an ensemble by voting rule. Through applying the model to classify data stream, the experiment results show that the ensemble method is feasible and effective.

A Method for Evaluation of Green Degree in Green Supply Chain

2010 ◽  
Vol 450 ◽  
pp. 433-436
Author(s):  
Bao Qin Yu ◽  
Yue Zhang
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Rough Set ◽  
Simulation Analysis ◽  
Ecological Impact ◽  
Classification Model ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Chain Management ◽  
Use Of Resources

Green supply chain management is a method to reduce the ecological impact of the whole supply chain and make the most efficient use of resources. In this paper, the data mining (DM) technique is utilised to evaluate green degree in green supply chain. Then the process of the evaluation is given and a classification model of multivariate decision tree based on rough set is proposed. Finally, a simulation analysis is given to show the application of this method.

scholarly journals A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

2016 ◽  
Vol 07 (01) ◽  
pp. 1-21 ◽  
Author(s):  
Nancy Jane ◽  
Kannan Arputharaj ◽  
Khanna Nehemiah
Keyword(s):  
Time Series ◽  
Rough Set ◽  
Classification Accuracy ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Classification Model ◽  
Experimental Result ◽  
Series Data ◽  
Forecasting Model ◽  
Neuro Fuzzy

SummaryClinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging.This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification.In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier.For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset.The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

Multivariate Decision Tree Algorithm Based on Rough Set

2012 ◽  
Vol 190-191 ◽  
pp. 347-351
Author(s):  
Bing Xiang Liu ◽  
Yan Wu ◽  
Meng Shan Li
Keyword(s):  
Decision Tree ◽  
Rough Set ◽  
Rough Sets ◽  
Inductive Learning ◽  
Classification Model ◽  
Classification Rules ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Rough Sets Theory ◽  
Sets Theory

The decision tree is a widely used classification model and inductive learning method based on examples. It is characterized by the simple classification rules, easy understanding for users and so on, but we also can see some disadvantages in certain situations. The paper puts forward the multivariable decision tree algorithm which based on a rough set to a combination of rough sets theory and decision tree algorithm. The multivariable decision tree algorithm has reduced the complexity of decision tree while not affect the readability of the classification rules. Experimental analysis has witnessed the feasibility and efficiency of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document