abstract = "In this paper, a feature transformation method for
two-class classification using genetic programming (GP)
is proposed. GP derives a transformation formula to
improve the classification accuracy of Support Vector
Machine, SVM. In this paper, we propose a weight
function to evaluate converted feature space and the
proposed function is used to evaluate the function of
GP. In the proposed function, the ideal two-class
distribution of items is assumed and the distance
between the actual and ideal distributions is
calculated. The weight is imposed to these distances.
To examine the effectiveness of the proposed function,
a numerical experiment was performed. In the
experiment, as the result, the classification accuracy
of the proposed method showed the better result than
that of the existing method.",
notes = "Fac. of Life & Med. Sci., Doshisha Univ., Kyotanabe,
Japan