Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Communications of the ACM
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
A new version of the rule induction system LERS
Fundamenta Informaticae
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multi-layer Perceptrons for Functional Data Analysis: A Projection Based Approach
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Classification of transcranial Doppler signals using their chaotic invariant measures
Computer Methods and Programs in Biomedicine
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Soft computing system for bank performance prediction
Applied Soft Computing
Expert Systems with Applications: An International Journal
Methodology of quantitative risk assessment for information system security
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
| Hi-index | 12.05 |
It is a problematic issue faced by the government sector to effectively discover potentially owed taxes (overdue payments) and continually promote the principle of taxation justices. However, due to the high economic development over the past 30years in Taiwan, the quantity of vehicles recorded contingent with the mounds of data generated and collected in the Tax Bureau is growing at a fast rate concurrently; therefore, the mission-critical nature of the data and the speed with which analyses need to be made now increase the requirements for a more reliable way to dig out a government's taxation information hidden. Based on the reasons above, this study proposes a hybrid model, which combines the Delphi method and rough sets classifier approaches, for intelligently classifying the vehicle license tax payment (called VLTP) to solve real-world problems that are faced by taxation agencies. The proposed hybrid model is illustrated by examining a practically collected dataset, and the experimental results reveal that this hybrid model outperforms the listing methods in terms of accuracy and its standard deviation. More importantly, the output created by rough sets LEM2 (Learning from Examples Module, version 2) algorithm is a set of comprehensible and meaningful rules applied readily in knowledge-based systems of payment classification of vehicle license tax for tax authority.