Research and Advances

Optimizing decision trees through heuristically guided search

Optimal decision table conversion has been tackled in the literature using two approaches, dynamic programming and branch-and-bound. The former technique is quite effective, but its time and space requirements are independent of how “easy” the given table is. Furthermore, it cannot be used to produce good, quasioptimal solutions. The branch-and-bound technique uses a good heuristic to direct the search, but is cluttered up by an enormous search space, since the number of solutions increases with the number of test variables according to a double exponential. In this paper we suggest a heuristically guided top-down search algorithm which, like dynamic programming, recognizes identical subproblems but which can be used to find both optimal and quasioptimal solutions. The heuristic search method introduced in this paper combines the positive aspects of the above two techniques. Compressed tables with a large number of variables can be handled without deriving expanded tables first.


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Research and Advances

An application of heuristic search methods to edge and contour detection

This paper presents a method for detecting edges and contours in noisy pictures. The properties of an edge are embedded in a figure of merit and the edge detection problem becomes the problem of minimizing the given figure of merit. This problem can be represented as a shortest path problem on a graph and can be solved using well-known graph search algorithms. The relations between this representation of the minimization problem and a dynamic programming approach are discussed, showing that the graph search method can lead to substantial improvements in computing time. Moreover, if heuristic search methods are used, the computing time will depend on the amount of noise in the picture. Some experimental results are given; these show how various information about the shape of the contour of an object can be embedded in the figure of merit, thus allowing the extraction of contours from noisy pictures and the separation of touching objects.

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