A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Gravity based spatial clustering
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hybridized rough set framework for classification: an experimental view
Design and application of hybrid intelligent systems
A Shrinking-Based Dimension Reduction Approach for Multi-Dimensional Data Analysis
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Customized classification learning based on query projections
Information Sciences: an International Journal
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
A shrinking-based approach for multi-dimensional data analysis
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A DGC-based data classification method used for abnormal network intrusion detection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
IEEE Transactions on Neural Networks
Financial Forecasting of Invoicing and Cash Inflow Processes for Fair Exhibitions
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Information Sciences: an International Journal
Transfer learning for cross-company software defect prediction
Information and Software Technology
Information Sciences: an International Journal
Data envelopment analysis classification machine
Information Sciences: an International Journal
Perceptual relativity-based local hyperplane classification
Neurocomputing
Gravitation based classification
Information Sciences: an International Journal
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Data gravitation based classification (DGC) is a novel data classification technique based on the concept of data gravitation. The basic principle of DGC algorithm is to classify data samples by comparing the data gravitation between the different data classes. In the DGC model, a kind of ''force'' called data gravitation between two data samples is computed. Data from the same class are combined as a result of gravitation. On the other hand, data gravitation between different data classes can be compared. A larger gravitation from a class means the data sample belongs to a particular class. One outstanding advantage of the DGC, in comparison with other classification algorithms is its simple classification principle with high performance. This makes the DGC algorithm much easier to be implemented. Feature selection plays an important role in classification problems and a novel feature selection algorithm is investigated based on the idea of DGC and weighted features. The proposed method is validated by using 12 well-known classification data sets from UCI machine learning repository. Experimental results illustrate that the proposed method is very efficient for data classification and feature selection.