A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems

A Survey on Classification and Rule Extraction Techniques for Datamining

“…Compared to that, hybrid GA-based classifier model, it is having good improvement in classification accuracy. In some technique, they can classify imbalance datasets [6], [7] and in some can solve multiclass classification problem [1], [3], [4], [8], [11]. But the hybrid GA-based classifier [20] can classify the imbalance dataset and also can handle multiclass classification problem.…”

“…In Hybrid GA-based classifier [20], GA can be considered as the best optimization technique for improving the accuracy of classification. Specific datasets are used for performance evaluation in the works of [1], [19] and [11]. But in Hybrid GA-based classifier [20], the performance is evaluated using many general data sets.…”

“…The main advantage among all the classification is that, it increases the classification accuracy. In many of the research works such as [1], [3], [4], [6], [8], [11], [13], [14] and [15], optimization technique was not used. In Hybrid GA-based classifier [20], GA can be considered as the best optimization technique for improving the accuracy of classification.…”

“…Kemel Polat et al [1] proposed a novel hybrid classifier to classify multi-class learning problems. The classifier is based on C4.5 decision tree classifier and one-against-all approach.…”

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

“…Decision trees are effectively applied when the number of object features is not great [7], [8]. Except random forests [9], boosting is applicable when the number of object features is in the order of hundreds or thousands [10], [11].…”