TY  - JOUR
KW  - Association rules; Data acquisition; Decision trees; Genetic algorithms; Predictive analytics
KW  -  Associative classification; Class association rules; Machine-learning database; Multiple-association; Overall accuracies; Parallel genetic algorithms; Pruning techniques; Unstructured data
KW  -  Trees (mathematics)
ID  - scholars10612
N2  - Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noisy and distorted data during data collection. To overcome the influences of low quality dataset, this research proposes a new optimized pruning technique to prune while optimizing the decision tree using genetic algorithm. To achieve the most ideal decision tree, fitness value is weighed using not only the accuracy of the class association rules but also with the size of the decision tree. The size is obtained from the number of nodes of that particular decision tree. However, the fitness value formula is not putting more weightage on the size of the tree as accuracy from a small decision tree will tend to overfit to the training data. Experiments were conducted using databases from UCI machine learning database repository and the results showed that the proposed prediction model is consistently reliable and has good overall accuracy. © Springer International Publishing AG 2018.
VL  - 5
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375471&doi=10.1007%2f978-3-319-59427-9_58&partnerID=40&md5=7091b25e89d6f201c6f8db6568b6e47f
JF  - Lecture Notes on Data Engineering and Communications Technologies
A1  - HanChern-Tong
A1  - Aziz, I.
Y1  - 2018///
SP  - 554
TI  - CMARPGA: Classification based on multiple association rules using parallel genetic algorithm pruned decision tree
N1  - cited By 1
AV  - none
EP  - 560
SN  - 23674512
PB  - Springer Science and Business Media Deutschland GmbH
ER  -