Background: Reliable and precise classification methods for tumor types have started to see
wide deployment, in particular in the area of cancer diagnosis and personalized cancer drug design.
The traditional Sparse Representation-based Classification (SRC) method can achieve high accuracy
for tumor classification but also suffer from inefficiency when handling noisy datasets. To resist such
disadvantage, some researchers proposed collaborative Representation–based Classification (CRC)
method, which is more efficient and less complex.
Method: In this paper, we design a novel Kernelized Convex Hull Collaborative Representation and
Classification (KCHCRC) approach to further improve it. Though modeling the testing sample as a
special convex hull with a single element, the convex hull can collaboratively be represented over the
whole training samples. When the represented coefficients are fixed, we can calculate the distance between
the testing sample and training samples with identical type for each category. To demonstrate
the performance of our approach, we compare with the prior state-of-the-art tumor classification methods
on various 11 tumor gene expression datasets.
Result: The experimental results show that our approach is efficacy and efficiency.