title: Improving Hoeffding Trees

authors: Kirkby, Richard Brendon

subjects: machine learning

abstract: Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

text: Improving Hoeffding Trees Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

year: 2007

title: Children in the physical collection: Implications for the digital library

authors: Cunningham, Sally Jo

subjects: computer science

abstract: Finding a good book can be difficult, particularly for young readers. This paper adds to our understanding of how children select books for recreational reading by exploring the ‘native’ strategies (both successful and ineffective) that children employ in physical bookstores and libraries. The insights gained from how children find and select books in the physical environment are applied to suggest design guidelines for a children's digital library.

text: Children in the physical collection: Implications for the digital library Finding a good book can be difficult, particularly for young readers. This paper adds to our understanding of how children select books for recreational reading by exploring the ‘native’ strategies (both successful and ineffective) that children employ in physical bookstores and libraries. The insights gained from how children find and select books in the physical environment are applied to suggest design guidelines for a children's digital library.

year: 2012

title: The 2020 cannabis referendum: Māori voter support, racialized policing, and the Criminal Justice System

authors: Dempster, Craig

abstract: In the New Zealand 2020 cannabis referendum, 50.7% of all voters rejected the creation of a legally-regulated cannabis market and instead supported retaining the current prohibitionist policy. Although the referendum failed to pass, a majority of Māori voted in favor of cannabis law reform. This paper suggests that within the Māori community there is a more nuanced appreciation of the impact of policing cannabis. Māori perceive that greater harm is caused by the racialized policing of cannabis than by the usage of it. Following McCreanor, et al. (2014), this paper employs a thematic, content analysis of the New Zealand Herald’s coverage of the 2020 cannabis referendum to investigate the presence of race-based targeting/policing in discussions of the legislation. The results reveal that racial disparities emerged as secondary to framing both the impact of cannabis and the referendum as race-neutral and affecting everyone in society equally. This paper argues that the impact of the policing of this particular drug impacts Māori differently, wherein they bear the brunt of racialized policing. Thus, Māori possess a more sophisticated understanding that warrants consideration because it is inextricably linked to lived experiences of policing that differ from wider social narratives of policing and drug policy in New Zealand.

text: The 2020 cannabis referendum: Māori voter support, racialized policing, and the Criminal Justice System In the New Zealand 2020 cannabis referendum, 50.7% of all voters rejected the creation of a legally-regulated cannabis market and instead supported retaining the current prohibitionist policy. Although the referendum failed to pass, a majority of Māori voted in favor of cannabis law reform. This paper suggests that within the Māori community there is a more nuanced appreciation of the impact of policing cannabis. Māori perceive that greater harm is caused by the racialized policing of cannabis than by the usage of it. Following McCreanor, et al. (2014), this paper employs a thematic, content analysis of the New Zealand Herald’s coverage of the 2020 cannabis referendum to investigate the presence of race-based targeting/policing in discussions of the legislation. The results reveal that racial disparities emerged as secondary to framing both the impact of cannabis and the referendum as race-neutral and affecting everyone in society equally. This paper argues that the impact of the policing of this particular drug impacts Māori differently, wherein they bear the brunt of racialized policing. Thus, Māori possess a more sophisticated understanding that warrants consideration because it is inextricably linked to lived experiences of policing that differ from wider social narratives of policing and drug policy in New Zealand.

year: 2022