Optimization techniques in data mining with applications to biomedical and psychophysiological data sets

Our research mainly consisted by two parts. First, apply p-norm error measure instead of 1-norm measure in a linear programming discrimination, which generates a linear hyperplane to classify two data sets. With this p-norm error measure, the errors generated by the classifier are not treated equally but rather biased. For 1, the bigger one error is, the more weight it obtains in the objective function.

Second, investigation is conducted on a psychophysiological data set. Various methods are tested on this multi-dimensional time-series data set, from the linear programming method to the neural network method. With the help of DFT, The data is able to be transferred from the time domain to the frequency domain, in which the data set has interesting patterns