Application of a New m Ant-MinerPR Algorithm in Classification Rule Mining
L. Yang, K.S. Li, W.S. Zhang, Y. Wang, Z.X. Ke
Journal of Digital Information Management, Vol. 13, No. 5, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i5/v13i5_1.pdf
Abstract:
A new classification algorithm based on
multi-ant and pheromone repulsion principles is studied
and proposed in this paper to improve the prediction
accuracy of the classification rule based on the traditional
Ant-Miner algorithm. The proposed algorithm uses multiant
colony construction method to reduce dependence
on random initial term. The volatile coefficient of
pheromone update is added to prevent premature
convergence. The new state transition function based on
pheromone repulsion principle is also presented to avoid
falling into local optimum, whereas a rule quality formula
is improved. Several aspects of the proposed algorithm
are investigated by experimenting of benchmark data sets
from the University of California at Irvine repository. We
study the performance of our proposed approach and
compare it with several commonly used classification
algorithms, such as Ant-Miner and Ant-Miner with graphical
user interface. Experimental results indicate that the
proposed approach builds more accurate models than
the compared algorithms. The high accuracy
supplemented by the comprehensibility of the discovered
rule sets is the main advantage of this method.A new classification algorithm based on
multi-ant and pheromone repulsion principles is studied
and proposed in this paper to improve the prediction
accuracy of the classification rule based on the traditional
Ant-Miner algorithm. The proposed algorithm uses multiant
colony construction method to reduce dependence
on random initial term. The volatile coefficient of
pheromone update is added to prevent premature
convergence. The new state transition function based on
pheromone repulsion principle is also presented to avoid
falling into local optimum, whereas a rule quality formula
is improved. Several aspects of the proposed algorithm
are investigated by experimenting of benchmark data sets
from the University of California at Irvine repository. We
study the performance of our proposed approach and
compare it with several commonly used classification
algorithms, such as Ant-Miner and Ant-Miner with graphical
user interface. Experimental results indicate that the
proposed approach builds more accurate models than
the compared algorithms. The high accuracy
supplemented by the comprehensibility of the discovered
rule sets is the main advantage of this method.A new classification algorithm based on
multi-ant and pheromone repulsion principles is studied
and proposed in this paper to improve the prediction
accuracy of the classification rule based on the traditional
Ant-Miner algorithm. The proposed algorithm uses multiant
colony construction method to reduce dependence
on random initial term. The volatile coefficient of
pheromone update is added to prevent premature
convergence. The new state transition function based on
pheromone repulsion principle is also presented to avoid
falling into local optimum, whereas a rule quality formula
is improved. Several aspects of the proposed algorithm
are investigated by experimenting of benchmark data sets
from the University of California at Irvine repository. We
study the performance of our proposed approach and
compare it with several commonly used classification
algorithms, such as Ant-Miner and Ant-Miner with graphical
user interface. Experimental results indicate that the
proposed approach builds more accurate models than
the compared algorithms. The high accuracy
supplemented by the comprehensibility of the discovered
rule sets is the main advantage of this method.