  The order of training samples can have a significant impact on the
performance of a classifier. Curriculum learning is a method of ordering
training samples from easy to hard. This paper proposes the novel idea of a
curriculum learning approach called Data Distribution-based Curriculum Learning
(DDCL). DDCL uses the data distribution of a dataset to build a curriculum
based on the order of samples. Two types of scoring methods known as DDCL
(Density) and DDCL (Point) are used to score training samples thus determining
their training order. DDCL (Density) uses the sample density to assign scores
while DDCL (Point) utilises the Euclidean distance for scoring. We evaluate the
proposed DDCL approach by conducting experiments on multiple datasets using a
neural network, support vector machine and random forest classifier. Evaluation
results show that the application of DDCL improves the average classification
accuracy for all datasets compared to standard evaluation without any
curriculum. Moreover, analysis of the error losses for a single training epoch
reveals that convergence is faster when using DDCL over the no curriculum
method.
