In this thesis we carry out a series of investigations into the relationship between diversity and combination methods and diversity and AdaBoost. In our first investigation we study the relationships between nine combination methods. Two data sets are used. We consider the overall accuracies of the combination methods, their improvement over the single best classifier, and the correlation between the ensemble outputs using the different combination methods. Next we introduce ten diversity measures. Using the same two data sets, we study the relationships between the diversity measures. Then we look at their relationship to the combination methods previously studied. The ranges of the ten diversity measures for three classifiers are derived. They are compared with the theoretical ranges and their implications for the accuracy of the ensemble are studied. We then proceed to investigate the diversity of classifier ensembles built using the AdaBoost algorithm. We carry out experiments with two datasets using ten-fold cross validation. We build 100 classifiers each time using linear classifiers, quadratic classifiers or neural networks. We study how diversity varies as the classifier ensemble grows and how the different types of classifier compare. Next we consider ways of improving AdaBoost's performance. We conduct an investigation into how modifying the size of the training sets and the complexity of the individual classifiers alter the ensemble's performance. We carry out experiments using three datasets. Lastly we consider using pareto optimality to determine which classifiers built by AdaBoost to add to the ensemble. We carry out experiments with ten datasets. We compare standard AdaBoost to AdaBoost with two versions of the Pareto-optimality method called Pareto 5 and Pareto 10, to see whether we can reduce the ensemble size without harming the ensemble accuracy.