Abstract—Due to the digital world explosion and its
appearance in everyday life, predicting numeric actions became
necessary in machine learning. However, due to growth of
interest in understanding how problems can be solved, simple
prediction algorithms are more helpful than the difficult
statistical approaches. Covering algorithm can be used to
accomplish difficult problems using simple rules or trees. One
family called RULES was found to be very interesting with
appealing properties. It is one of the most flexible and simplest
families with high learning rate. Nevertheless, even though
RULES is actively improving but it is surprisingly neglected,
especially with numerical datasets. Thus, the purpose of this
paper is to extend the literature and investigate the problems of
continuous classes in RULES and other inductive learning
families. A theoretical analysis is conducted to show the effect of
numerical actions and how it is still an open research area. An
empirical evaluation is also provided to prove how RULES
family can be used as the base of further improvement.
Accordingly, this paper can be used as a reference by
researchers to know what research area is still not covered and
need further refinement in inductive learning.
Index Terms—Continuous classes, covering algorithms, decision tree, inductive learning, RULES family.
Hebah ElGibreen is with Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia (e-mail: firstname.lastname@example.org).
Mehmet Sabih Aksoy is with Information System Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia (e-mail: email@example.com).
Cite: Hebah ElGibreen and Mehmet Sabih Aksoy, "Inductive Learning for Continuous Classes and the Effect of RULES Family," International Journal of Information and Education Technology vol. 5, no. 8, pp. 564-570, 2015.