Abstract
This paper introduces a novel classification method-transductive total least square classification method (TTLSC). While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively – using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The total least square method (TLS) is one of the optimal fitting methods that can be used for curve and surface fitting and outperform the commonly used least square fitting methods in resisting both normal noise and outlier. The TTLSC is illustrated by a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modelling can also be applied to solve other classification or clustering problems.
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Song, Q., Ma, T.M., Kasabov, N. (2006). TTLSC – Transductive Total Least Square Model for Classification and Its Application in Medicine. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_22
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DOI: https://doi.org/10.1007/11811305_22
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