Murthy, KRK and Keerthi, SS and Murty, MN
(2001)
*Rule prepending and post-pruning approach to incremental learning of decision lists.*
In: Pattern Recognition, 34
(8).
pp. 1697-1699.

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## 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 $(N_k)$.

Item Type: | Journal Article |
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Related URLs: | |

Additional Information: | Copyright of this article belongs to Elsevier. |

Keywords: | Decision list;Incremental learning;Rule induction;CDL4;Decision list pruning |

Department/Centre: | Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation) |

Date Deposited: | 30 Mar 2007 |

Last Modified: | 19 Sep 2010 04:36 |

URI: | http://eprints.iisc.ernet.in/id/eprint/10488 |

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