A Modal Symbolic Classifier for Interval Data
- Cite this paper as:
- Silva F.C.D., de A.T. de Carvalho F., de Souza R.M.C.R., Silva J.Q. (2006) A Modal Symbolic Classifier for Interval Data. In: King I., Wang J., Chan LW., Wang D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg
A modal symbolic classifier for interval data is presented. The proposed method needs a previous pre-processing step to transform interval symbolic data into modal symbolic data. The presented classifier has then as input a set of vectors of weights. In the learning step, each group is also described by a vector of weight distributions obtained through a generalization tool. The allocation step uses the squared Euclidean distance to compare two modal descriptions. To show the usefulness of this method, examples with synthetic symbolic data sets are considered.
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