Learning decision tree classifiers
ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Set Based Data Exploration Using ROSE System
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Granular Computing on Binary Relations
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A comparative assessment of classification methods
Decision Support Systems
Computers and Operations Research
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
Rough Set Based Feature Selection for Web Usage Mining
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
A frequency assessment expert system of piezoelectric transducers in paucity of data
Expert Systems with Applications: An International Journal
The characteristics of learning in limited data and the comparative assessment of learning methods
WSEAS Transactions on Information Science and Applications
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
IEEE Transactions on Neural Networks
A bit-chain based algorithm for problem of attribute reduction
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
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While learning with data having large number of attribute, a system is easy to freeze or shut down or run for a long time. Therefore, the proposed Binary Conversion (BC) is a novel method to solve this kind of large attribute problem in machine learning. The purpose of BC is to reduce data dimensions by a binary conversion process. All the attributes are reserved but combined into few numbers of new attributes instead of that some attributes are removed. To prevent the information loss problem during the conversion, each binary type data value occupies its own digital position in BC. In addition, 4 data sets: nbuses, ACLP, MONK3, and Buseskod data are used in this study to test and compare the learning accuracies and learning time. The results indicate that the proposed BC can keep about the same level of accuracy but increase the learning efficiency.