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: Promoting community socio-ecological sustainability through technology: A case study from Chile

authors: Aguayo Corvalan, Claudio

subjects: community education

abstract: The importance of community learning in effecting social change towards ecological sustainability has been recognised for some time. More recently, the use of Information and Communication Technology (ICT) tools to promote socio-ecological sustainability has been shown to have potential in community education for sustainable development (ESD). The effective design and use of technology for community learning implies an understanding of a range of cross- dimensional factors including: socio-cultural characteristics and needs of the target audience; considerations of available and culturally responsive types of technology; and non-formal pedagogical ESD strategies for community empowerment. In addition, both technology itself and social communities are dynamically evolving and complex entities. This article presents a case study which evaluated the potential of ICT for promoting ecological literacy and action competence amongst community members in southern Chile. The case study addressed the ecological deterioration of a lake, which is having deep social, economic, recreational and cultural implications locally. The authors’ research involved developing a theoret- ical framework for the design, implementation and use of ICT for community learning for sustainability. The framework was based on key ideas from ESD, ICT and community education, and was underpinned by a systems thinking approach to account for the dynamism and complexity of such settings. Activity theory provided a frame to address overarching socio-cultural elements when using technology as a mediating tool for community learning. The authors’ findings suggest that the use of an ICT tool, such as a website, can enhance ecological literacy in relation to a local socio-ecological issue.

text: Promoting community socio-ecological sustainability through technology: A case study from Chile The importance of community learning in effecting social change towards ecological sustainability has been recognised for some time. More recently, the use of Information and Communication Technology (ICT) tools to promote socio-ecological sustainability has been shown to have potential in community education for sustainable development (ESD). The effective design and use of technology for community learning implies an understanding of a range of cross- dimensional factors including: socio-cultural characteristics and needs of the target audience; considerations of available and culturally responsive types of technology; and non-formal pedagogical ESD strategies for community empowerment. In addition, both technology itself and social communities are dynamically evolving and complex entities. This article presents a case study which evaluated the potential of ICT for promoting ecological literacy and action competence amongst community members in southern Chile. The case study addressed the ecological deterioration of a lake, which is having deep social, economic, recreational and cultural implications locally. The authors’ research involved developing a theoret- ical framework for the design, implementation and use of ICT for community learning for sustainability. The framework was based on key ideas from ESD, ICT and community education, and was underpinned by a systems thinking approach to account for the dynamism and complexity of such settings. Activity theory provided a frame to address overarching socio-cultural elements when using technology as a mediating tool for community learning. The authors’ findings suggest that the use of an ICT tool, such as a website, can enhance ecological literacy in relation to a local socio-ecological issue.

year: 2017

title: Ati Ue: Te Aitanga a Tuoro

authors: James, Steven (Tipene) Robert

subjects: Ngati Whakaue

abstract: This thesis is based on the manuscripts of my tupuna Tuoro Akapita Pango that are held in a treasured whānau box. The manuscripts contain history and kōrero of Te Arawa written by multiple generations of the whānau Pango. Within this thesis I explore the value of inter-generational knowledge through considering what lies within these whānau manuscripts and taking the opportunity to discover more history about Ngāti Whakaue through a whānau lens. Researching these manuscripts enables reconnection of the past and the present; the reconnection between forgotten knowledge and whānau. 
Specifically, this thesis proposes a Ngāti Whakaue approach to engaging historical materials and research. It centres the voices of the people, such as the many family members involved within the process, as well as the box of writings and the many gems that hide within. Also, it asks what the manuscripts mean for the current descendants involved in the research. 
The research has been a means of reconnecting with the past to give relevance and context to the present. The objective is demonstrating the value of iwi-specific research methods so that whānau feel encouraged to embark on their own journey.

text: Ati Ue: Te Aitanga a Tuoro This thesis is based on the manuscripts of my tupuna Tuoro Akapita Pango that are held in a treasured whānau box. The manuscripts contain history and kōrero of Te Arawa written by multiple generations of the whānau Pango. Within this thesis I explore the value of inter-generational knowledge through considering what lies within these whānau manuscripts and taking the opportunity to discover more history about Ngāti Whakaue through a whānau lens. Researching these manuscripts enables reconnection of the past and the present; the reconnection between forgotten knowledge and whānau. 
Specifically, this thesis proposes a Ngāti Whakaue approach to engaging historical materials and research. It centres the voices of the people, such as the many family members involved within the process, as well as the box of writings and the many gems that hide within. Also, it asks what the manuscripts mean for the current descendants involved in the research. 
The research has been a means of reconnecting with the past to give relevance and context to the present. The objective is demonstrating the value of iwi-specific research methods so that whānau feel encouraged to embark on their own journey.

year: 2021