title: Logistic model trees

authors: Landwehr, Niels

subjects: computer science

abstract: Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into `model trees', i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.

text: Logistic model trees Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into `model trees', i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.

year: 2005

title: Efficient online evaluation of big data stream classifiers

authors: Bifet, Albert

subjects: Data Streams

abstract: The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.

text: Efficient online evaluation of big data stream classifiers The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.

year: 2015

title: Outdoor learning in Aotearoa New Zealand: voices past, present, and future

authors: Cosgriff, Marg

subjects: culture

abstract: Many of the principles and practices that have influenced outdoor education in Aotearoa New Zealand find their genesis in the United Kingdom and North America. In recent times, many of these foundational assumptions have been called into question. This paper highlights how emerging 'local' voices are questioning and reframing how outdoor education is conceptualised and practiced. In large part this is due to a sense of distinctiveness borne from the bicultural foundations that underpin governance and policy-making. This paper explores how outdoor educators are developing pedagogies that acknowledge the particularities of our context, particularly the bicultural foundation of Aotearoa New Zealand. The paper highlights how social and cultural influences shape educational policy and how outdoor educators are responding, both theoretically and practically, to meet the needs of learners in an increasingly diverse society.

text: Outdoor learning in Aotearoa New Zealand: voices past, present, and future Many of the principles and practices that have influenced outdoor education in Aotearoa New Zealand find their genesis in the United Kingdom and North America. In recent times, many of these foundational assumptions have been called into question. This paper highlights how emerging 'local' voices are questioning and reframing how outdoor education is conceptualised and practiced. In large part this is due to a sense of distinctiveness borne from the bicultural foundations that underpin governance and policy-making. This paper explores how outdoor educators are developing pedagogies that acknowledge the particularities of our context, particularly the bicultural foundation of Aotearoa New Zealand. The paper highlights how social and cultural influences shape educational policy and how outdoor educators are responding, both theoretically and practically, to meet the needs of learners in an increasingly diverse society.

year: 2012