In difficult classification problems of the zdimensional
points into two groups giving 01 responses due to the messy data
structure, we try to find the denser regions for the favorable
customers of response 1, instead of finding the boundaries to separate
the two groups. Such regions are called the bumps, and finding
the boundaries of the bumps is called the bump hunting. The main
objective of this paper is to find the largest region of the bumps
under a specified ratio of the number of the points of response
1 to the total. Then, we may obtain a tradeoff curve between the
number of points of response 1 and the specified ratio. The decision
tree method with the Gini's index will provide the simpleshaped
boundaries for the bumps if the marginal density for response 1
shows a rather simple or monotonic shape. Since the computing time
searching for the optimal trees will cost much because of the NPhardness
of the problem, some random search methods, e.g., the genetic algorithm
adapted to the tree, are useful. Due to the existence of many local
maxima unlike the ordinary genetic algorithm search results, the
extremevalue statistics will be useful to estimate the global
optimum number of captured points; this also guarantees the accuracy
of the semioptimal solution with the simple descriptive rules.
This combined method of genetic algorithm search and extremevalue
statistics use is new. We apply this method to some artificial
messy data case which mimics the real customer database, showing
a successful result. The reliability of the solution is discussed.

data
mining, data science, bump hunting, genetic algorithm, extremevalue
statistics, tradeoff curve, decision tree, bootstrap

