title: Instance-based learning: nearest neighbour with generalisation

authors: Martin, Brent

subjects: computer science

abstract: Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest neighbour learning is not without problems. It is difficult to define a distance function that works well for both discrete and continuous attributes. Noise and irrelevant attributes also pose problems. Finally, the specificity bias adopted by instance-based learning, while often an advantage, can over-represent small rules at the expense of more general concepts, leading to a marked decrease in classification performance for some domains. Generalised exemplars offer a solution. Examples that share the same class are grouped together, and so represent large rules more fully. This reduces the role of the distance function to determining the class when no rule covers the new example, which reduces the number of classification errors that result from inaccuracies of the distance function, and increases the influence of large rules while still representing small ones. This thesis investigates non-nested generalised exemplars as a way of improving the performance of nearest neighbour. The method is tested using benchmark domains and the results compared with documented results for ungeneralised exemplars, nested generalised exemplars, rule induction methods and a composite rule induction and nearest neighbour learner. The benefits of generalisation are isolated and the performance improvement measured. The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time.

text: Instance-based learning: nearest neighbour with generalisation Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest neighbour learning is not without problems. It is difficult to define a distance function that works well for both discrete and continuous attributes. Noise and irrelevant attributes also pose problems. Finally, the specificity bias adopted by instance-based learning, while often an advantage, can over-represent small rules at the expense of more general concepts, leading to a marked decrease in classification performance for some domains. Generalised exemplars offer a solution. Examples that share the same class are grouped together, and so represent large rules more fully. This reduces the role of the distance function to determining the class when no rule covers the new example, which reduces the number of classification errors that result from inaccuracies of the distance function, and increases the influence of large rules while still representing small ones. This thesis investigates non-nested generalised exemplars as a way of improving the performance of nearest neighbour. The method is tested using benchmark domains and the results compared with documented results for ungeneralised exemplars, nested generalised exemplars, rule induction methods and a composite rule induction and nearest neighbour learner. The benefits of generalisation are isolated and the performance improvement measured. The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time.

year: 1995-05

title: Climate change is here:  Teachers’ and students’ perceptions about education for It

authors: Bevins, Patricia Anne

subjects: Climate change

abstract: Climate change is the greatest threat humans have ever faced. The unprecedented anthropogenic activity impacting on the environment is having catastrophic effects across the globe and could continue for thousands of years. Mitigation to prevent further environmental harm and adaptation to the already-changing conditions are essential strategies in addressing climate change and, therefore, education for these strategies is imperative. Educators have an opportunity to prepare our learners with the knowledge and skills they will need to combat the impacts and effects of climate change. Climate change education offers young people a chance to develop their knowledge, critical and creative thinking and problem-solving skills while building their resilience and adaptive capacity to act in this crisis. But are our young people getting these learning opportunities?
This was an interpretive study that focused on the perceptions of climate change and climate change education of students, teachers and leaders at a secondary school in the Waikato region of New Zealand. A mix of quantitative and qualitative data was gathered by using an on-line questionnaire and focus groups with students and semi-structured interviews with their teachers and leaders. The data were divided into four sections: beliefs, knowledge, attitudes and actions for both the student data and the teachers’ and leaders’ data, and were thematically analysed using NVivo. 
The data clearly showed in this study that the students were worried about how climate change was going to affect their future lives and the lives of the people closest to them and most wanted to change their behaviour and take action to try and reduce the effects of a changing climate. In order to do this, the students wanted to learn more about climate change, raise their awareness, and the awareness in their community, so that everybody could be prepared for the changing climate. The findings indicated that the teachers all agreed that climate change education is important for students and wanted to know more about climate change so they could inform their students about climate change through education. The leaders indicated that it is vital that the students’ are thoroughly prepared for the changing climate and a change in behaviour, but they also felt that the teachers needed to be supported as well, and not just those teaching about climate change, but to make sure everyone is prepared for an uncertain future. 
This study recommends the Ministry of Education to firstly, consider how climate change education can be made available to all children and young people, and then to provide guidance on how educators in schools and teacher education throughout New Zealand could teach about climate change to all our learners.

text: Climate change is here:  Teachers’ and students’ perceptions about education for It Climate change is the greatest threat humans have ever faced. The unprecedented anthropogenic activity impacting on the environment is having catastrophic effects across the globe and could continue for thousands of years. Mitigation to prevent further environmental harm and adaptation to the already-changing conditions are essential strategies in addressing climate change and, therefore, education for these strategies is imperative. Educators have an opportunity to prepare our learners with the knowledge and skills they will need to combat the impacts and effects of climate change. Climate change education offers young people a chance to develop their knowledge, critical and creative thinking and problem-solving skills while building their resilience and adaptive capacity to act in this crisis. But are our young people getting these learning opportunities?
This was an interpretive study that focused on the perceptions of climate change and climate change education of students, teachers and leaders at a secondary school in the Waikato region of New Zealand. A mix of quantitative and qualitative data was gathered by using an on-line questionnaire and focus groups with students and semi-structured interviews with their teachers and leaders. The data were divided into four sections: beliefs, knowledge, attitudes and actions for both the student data and the teachers’ and leaders’ data, and were thematically analysed using NVivo. 
The data clearly showed in this study that the students were worried about how climate change was going to affect their future lives and the lives of the people closest to them and most wanted to change their behaviour and take action to try and reduce the effects of a changing climate. In order to do this, the students wanted to learn more about climate change, raise their awareness, and the awareness in their community, so that everybody could be prepared for the changing climate. The findings indicated that the teachers all agreed that climate change education is important for students and wanted to know more about climate change so they could inform their students about climate change through education. The leaders indicated that it is vital that the students’ are thoroughly prepared for the changing climate and a change in behaviour, but they also felt that the teachers needed to be supported as well, and not just those teaching about climate change, but to make sure everyone is prepared for an uncertain future. 
This study recommends the Ministry of Education to firstly, consider how climate change education can be made available to all children and young people, and then to provide guidance on how educators in schools and teacher education throughout New Zealand could teach about climate change to all our learners.

year: 2020

title: Wearable technology for hazardous remote environments: Smart shirt and Rugged IoT network for forestry worker health

authors: Hinze, Annika

subjects: software engineering

abstract: This paper introduces the architecture and details of our wearable IoT solution for workplace health and safety in rugged outdoor environments. We focus on the specific requirements defined by the New Zealand forestry environment as it is the industry with the most fatalities and highest accident rate in New Zealand. Neither consumer-level products nor professional wearables have been found to be suitable for forestry conditions. Furthermore, forestry workplaces cannot rely on existing networking infrastructure, and due to their remote and rugged nature, no permanent setup is possible.

We describe the Hakituri project in which we integrate wearable devices, networking, data analytics, and a buddy system into a novel monitoring solution for worker health and safety. Our wearable IoT solution includes a dynamic network set-up, wearables suitable for forestry conditions, contextual rule-based data analytics, and real-time alerts. A wearable smart shirt carries sensors for collecting personal data from forestry workers, while further environmental sensors capture contextual data. The IoT network is set up for communication in unreliable and rugged outdoor environments. We explore a variety of setups to achieve the best quality outcomes for health and safety in the forestry domain.

text: Wearable technology for hazardous remote environments: Smart shirt and Rugged IoT network for forestry worker health This paper introduces the architecture and details of our wearable IoT solution for workplace health and safety in rugged outdoor environments. We focus on the specific requirements defined by the New Zealand forestry environment as it is the industry with the most fatalities and highest accident rate in New Zealand. Neither consumer-level products nor professional wearables have been found to be suitable for forestry conditions. Furthermore, forestry workplaces cannot rely on existing networking infrastructure, and due to their remote and rugged nature, no permanent setup is possible.

We describe the Hakituri project in which we integrate wearable devices, networking, data analytics, and a buddy system into a novel monitoring solution for worker health and safety. Our wearable IoT solution includes a dynamic network set-up, wearables suitable for forestry conditions, contextual rule-based data analytics, and real-time alerts. A wearable smart shirt carries sensors for collecting personal data from forestry workers, while further environmental sensors capture contextual data. The IoT network is set up for communication in unreliable and rugged outdoor environments. We explore a variety of setups to achieve the best quality outcomes for health and safety in the forestry domain.

year: 2022