A large volume of data has become commonplace in many domains these days. Machine learning algorithms can be trained to look for any useful hidden patterns in such data. Sometimes, these big data might need to be summarized to make them into a manageable size, for example by using histograms, for various reasons. Traditionally, machine learning algorithms can be trained on data expressed as real numbers and/or categories but not on a complex structure such as histogram. Since machine learning algorithms that can learn from data with histograms have not been explored to a major extent, this thesis intends to further explore this domain.

This thesis has been limited to classification algorithms, tree-based classifiers such as decision trees, and random forest in particular. Decision trees are one of the simplest and most intuitive algorithms to train. A single decision tree might not be the best algorithm in term of its predictive performance, but it can be largely enhanced by considering an ensemble of many diverse trees as a random forest. This is the reason why both algorithms were considered. So, the objective of this thesis is to investigate how one can adapt these algorithms to make them learn better on histogram data. Our proposed approach considers the use of multiple bins of a histogram simultaneously to split a node during the tree induction process. Treating bins simultaneously is expected to capture dependencies among them, which could be useful. Experimental evaluation of the proposed approaches was carried out by comparing them with the standard approach of growing a tree where a single bin is used to split a node. Accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) metrics along with the average time taken to train a model were used for comparison. For experimental purposes, real-world data from a large fleet of heavy duty trucks were used to build a component-failure prediction model. These data contain information about the operation of trucks over the years, where most operational features are summarized as histograms. Experiments were performed further on the synthetically generated dataset. From the results of the experiments, it was observed that the proposed approach outperforms the standard approach in performance and compactness of the model but lags behind in terms of training time. This thesis was motivated by a real-life problem encountered in the operation of heavy duty trucks in the automotive industry while building a data driven failure-prediction model. So, all the details about collecting and cleansing the data and the challenges encountered while making the data ready for training the algorithm have been presented in detail.

Data mining and machine learning algorithms are trained on large datasets to find useful hidden patterns. These patterns can help to gain new insights and make accurate predictions. Usually, the training data is structured in a tabular format, where the rows represent the training instances and the columns represent the features of these instances. The feature values are usually real numbers and/or categories. As very large volumes of digital data are becoming available in many domains, the data is often summarized into manageable sizes for efficient handling. To aggregate data into histograms is one means to reduce the size of the data. However, traditional machine learning algorithms have a limited ability to learn from such data, and this thesis explores extensions of the algorithms to allow for more effective learning from histogram data.

The thesis focuses on the decision tree and random forest algorithms, which are easy to understand and implement. Although, a single decision tree may not result in the highest predictive performance, one of its benefits is that it often allows for easy interpretation. By combining many such diverse trees into a random forest, the performance can be greatly enhanced, however at the cost of reduced interpretability. By first finding out how to effectively train a single decision tree from histogram data, these findings could be carried over to building robust random forests from such data. The overarching research question for the thesis is: How can the random forest algorithm be improved to learn more effectively from histogram data, and how can the resulting models be interpreted? An experimental approach was taken, under the positivist paradigm, in order to answer the question. The thesis investigates how the standard decision tree and random forest algorithms can be adapted to make them learn more accurate models from histogram data. Experimental evaluations of the proposed changes were carried out on both real world data and synthetically generated experimental data. The real world data was taken from the automotive domain, concerning the operation and maintenance of heavy-duty trucks. Component failure prediction models were built from the operational data of a large fleet of trucks, where the information about their operation over many years have been summarized as histograms. The experimental results showed that the proposed approaches were more effective than the original algorithms, which treat bins of histograms as separate features. The thesis also contributes towards the interpretability of random forests by evaluating an interactive visual tool for assisting users to understand the reasons behind the output of the models.