On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An analytic approach for obtaining maximal entropy OWA operator weights
Fuzzy Sets and Systems
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
A selective sampling approach to active feature selection
Artificial Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fuzzy clustering-based on aggregate attribute method
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling prioritized multicriteria decision making
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
On the properties of OWA operator weights functions with constant level of orness
IEEE Transactions on Fuzzy Systems
Neural-network feature selector
IEEE Transactions on Neural Networks
Induced and uncertain heavy OWA operators
Computers and Industrial Engineering
Decision-making with distance measures and induced aggregation operators
Computers and Industrial Engineering
An OWA-TOPSIS method for multiple criteria decision analysis
Expert Systems with Applications: An International Journal
Fuzzy induced generalized aggregation operators and its application in multi-person decision making
Expert Systems with Applications: An International Journal
Decision-making in sport management based on the OWA operator
Expert Systems with Applications: An International Journal
A unified model between the weighted average and the induced OWA operator
Expert Systems with Applications: An International Journal
Uncertain induced aggregation operators and its application in tourism management
Expert Systems with Applications: An International Journal
EDA-USL: unsupervised clustering algorithm based on estimation of distribution algorithm
International Journal of Wireless and Mobile Computing
Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure
Expert Systems with Applications: An International Journal
Similarity classifier with ordered weighted averaging operators
Expert Systems with Applications: An International Journal
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
Hi-index | 12.06 |
Information classification is an important role in decision-making problems. As information technology advances, large amounts of information stored in database. Many tasks are worked out in high complexity and dimensionality in classification problem. Therefore, the paper applies ordered weighted averaging (OWA) operator to fusion multi-attribute data into the aggregated values of single attribute, and cluster the aggregated values for classification tasks. The proposed method consists of four steps: (1) use stepwise regression to select and order the important attribute, (2) utilize OWA operator to get aggregated values of single attribute from multi-attribute data, (3) cluster the aggregated values by K-means method, (4) predict the clusters of testing data. In verification and comparison, three datasets: (1) Iris, (2) Wisconsin-breast-cancer, and (3) Key Performance Indicators datasets are conducted by the proposed method. The problems of high complexity and dimensionality are solved and the classification accuracy rate is higher than some existing methods.