classification accuracy [17, 18]. This ensures that the results are based on the consensus decision of the base classifiers. The base classifiers operate concurrently during the classification and their outputs are integrated to obtain the final output [18]. The model for the committee of classifiers is shown in figure 2. 

reached, the accuracy tends to remain constant. Nevertheless, the size of 21 was found to be optimal since it produced the best accuracy. The results obtained are found to be concurrent with literature. Currently the optimal size of an ensemble is 25 [18, 20]. Therefore, an ensemble size of 21 is used for evaluating the relationship ![0_image_0.png](0_image_0.png)

There are many aggregation methods that can be used to combine the outcomes of classifiers. These were explored in the preliminary report. The ensemble outcomes were all aggregated using simple majority voting. This was chosen because it is popular and easy to implement [9]. The outcomes of each individual from an ensemble are first converted to 0 or 1 using 0.5 as a threshold. The majority voting method chooses the prediction that is mostly predicted by different classifiers [19]. The other method that was implemented was averaging. All the outcomes from all the classifiers are taken and averaged. 

## 6.3. **Evaluation Of Optimal Ensemble Size**

It is important to use the optimal size of an ensemble that results in better generalisation of the data [20]. The ensemble size is determined by the number of classifiers that belong to an ensemble. The created classifiers were used to carry out this experiment. The ensemble size was incremented by one from 1 to 50. However, the structure of the networks was made to be different by varying the hidden nodes as the ensemble size increases. Hence, the size of the network itself is increased as the number of classifiers in the ensemble increases [4]. Figure 3 below shows the results obtained. It was however observed that the relationship between the size and accuracy of the ensemble depends on the accuracy of the individual classifiers that belong to the ensemble. Increasing the size of the neural network by increasing the hidden nodes tends to improve the classification accuracy as the number of the classifiers in an ensemble increases. However, an increase in size results in an increase in the prediction accuracy. Consequently, after the optimal size of 19 classifiers is Currently, measuring the outcome diversity had been popular than measuring the structural diversity [6]. It was however necessary to measure the outcome diversity for this study. This is because it is essential to measure the degree of the agreement and disagreement on the outcomes of the ensemble. This experiment was useful for analysing the limitations on structural diversity results. The diversity measure such as Q statistics was used to measure diversity. 

Q statistics evaluate the degree of similarity and dissimilarity in the outcomes of the classifiers within the ensemble [8]. The diversity index ranges from -1 to 1 where 0 indicates the highest diversity and 1 indicate lowest diversity [6]. For all 21 classifiers in an ensemble, each classifier is paired with every other classifier within the ensemble. The results from this study show that outcomes of the structurally diverse classifiers within the ensemble are highly correlated. This is indicated by a Q value which is closer to 1. The obtained Q value is from 0.88 to 0.91. 

## 6.5. **Evaluation Of Structural Diversity**

The created classifiers were used to investigate the relationship between the diversity and accuracy. There were ten base classifiers or species that were selected from the created classifiers which are all structurally different based only on the hidden nodes and activation functions. These networks had different activation function and hidden nodes were varied from 10 to 55 in 