"An Incremental Learning Algorithm for Constructing Boolean
Functions From Positive and Negative Examples"
Computers and Operations Research,
2002, (July 2002 issue), Vol. 29, No. 12, pp. 1677-1700.
Salvador Nieto Sanchez, Evangelos Triantaphyllou, Jianhua Chen
and T. Warren Liao
Scope and Purpose (special requirement by the Comps & OR
There is a growing need for methods that can analyze information
and infer patterns in a way that can be useful to the analyst.
This is the core of data mining and knowledge discovery from
databases. Such methods often infer a Boolean function from
observations that belong to different classes. This paper
presents a methodology that infers a Boolean function in an
incremental setting. The proposed approach can be used in
conjunction with existing algorithms that infer a Boolean
function from examples. The paper presents both algorithmic
developments and also an extensive empirical study. The results
presented in the paper suggest that the proposed incremental
approach is highly promising.
This paper introduces an incremental algorithm for
learning a Boolean function from examples. The functions are
constructed in the Disjunctive Normal Form (DNF) or the
Conjunctive Normal Form (CNF) and emphasis is placed in inferring
functions with as few clauses as possible. This incremental
algorithm can be combined with any existing algorithm that infers
a Boolean function from examples. In this paper it is combined
with the One Clause At a Time (OCAT) approach
[Triantaphyllou, et al., 1994] and [Triantaphyllou, 1994]
which is a non-incremental learning approach. An extensive
computational study was undertaken to assess the performance
characteristics of the new approach. As examples we used binary
vectors that represent text documents from different categories
from the TIPSTER collection. The computational results indicate
that the new algorithm is considerably more efficient and it
derives more accurate Boolean functions. As it was anticipated,
the Boolean functions (in DNF or CNF form) derived by the new
algorithm are comprised by more clauses than the functions
derived by the non-incremental approach.
Incremental and non-incremental learning, learning from examples,
Boolean functions, DNF and CNF form, machine learning, data
mining, the One Clause At a Time (OCAT) approach for inferring a