Classification with incomplete survey data: a Hopfield neural network approach

Classification with incomplete survey data: a Hopfield neural network approach

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Article ID: iaor20062897
Country: United Kingdom
Volume: 32
Issue: 10
Start Page Number: 2583
End Page Number: 2594
Publication Date: Oct 2005
Journal: Computers and Operations Research
Authors:
Keywords: fuzzy sets, statistics: general
Abstract:

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.

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