On the Pattern Recognition of Noisy Subsequence Trees

• Authors: B. John Oommen;Richard K.S. Loke
• Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
• Year: 2001

In this paper, we consider the problem of recognizing ordered labeled trees by processing their noisy subsequence-trees which are “patched-up” noisy portions of their fragments. We assume that we are given H, a finite dictionary of ordered labeled trees. $\rm X^*$ is an unknown element of H, and U is any arbitrary subsequence-tree of $\rm X^*$. We consider the problem of estimating $\rm X^*$ by processing Y, which is a noisy version of U. The solution which we present is, to our knowledge, the first reported solution to the problem. We solve the problem by sequentially comparing Y with every element X of H, the basis of comparison being a new dissimilarity measure between two trees, which implicitly captures the properties of the corrupting mechanism (“channel”) which noisily garbles U into Y. The algorithm which incorporates this constraint has been used to test our pattern recognition system yielding a remarkable accuracy. Experimental results which involve manually constructed trees of sizes between 25 and 35 nodes, and which contain an average of 21.8 errors per tree demonstrate that the scheme has about 92.8 percent accuracy. Similar experiments for randomly generated trees yielded an accuracy of 86.4 percent.