Statistical analysis with missing data
Statistical analysis with missing data
Collective computation in neuronlike circuits
Scientific American
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Component-based data mining frameworks
Communications of the ACM
Hopfield neural networks for timetabling: formulations, methods, and comparative results
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Modelling competitive Hopfield networks for the maximum clique problem
Computers and Operations Research
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications: An International Journal
Towards optimal use of incomplete classification data
Computers and Operations Research
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
Artificial Intelligence Review
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
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Survey data are often incomplete. Classification with incomplete survey data is a new subject. This study proposes a Hopfield neural network based model of classification for incomplete survey data. Using this model, an incomplete pattern is translated into fuzzy patterns. These fuzzy patterns, along with patterns without missing values, are then used as the exemplar set for teaching the Hopfield neural network. The classifier also retains information of fuzzy class membership for each exemplar pattern. When presenting a test sample, the neural network would find an exemplar that best matches the test pattern and give the classification result. Compared with other classification techniques, the proposed method can utilize more information provided by the data with missing values, and reveal the risk of the classification result on the individual observation basis.