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Rule prepending and post-pruning approach to incremental learning of decision lists

By KRK Murthy, SS Keerthi and MN Murty

Abstract

A decision list [1], DL, is defined as a list of ordered pairs $\{(T_1,V_1), (T_2,V_2),... , (T_r,V_r)\}$. These pairs are called nodes and they are denoted as $N_1,N_2,...,N_r$, where $N_i=(T_i,V_i). N_r$ is called default node of DL. Each $T_i$ is a test whose outcome is either True or False, each $V_i$ is a class label, and $T_r$ is the constant function, True. DL defines a classification function as follows: for any input x, DL(x) is defined to be equal to $V_j$, where j is the least index such that $T_j(x)$ = True. We denote the index of node $N_k$ as Index $(N_k)$, i.e. k=Index $(N_k)$

Topics: Computer Science & Automation
Publisher: Elsevier
Year: 2001
OAI identifier: oai:eprints.iisc.ac.in:10488
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