title: Adaptive random forests for evolving data stream classification

authors: Gomes, Heitor Murilo

subjects: Data stream mining

abstract: Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.

text: Adaptive random forests for evolving data stream classification Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.

year: 2017

title: Environmental education in New Zealand schools

authors: Bolstad, Rachel

subjects: New Zealand

abstract: This research, presented in four volumes, looked at current practice in environmental education in New Zealand schools using a range of methods.

text: Environmental education in New Zealand schools This research, presented in four volumes, looked at current practice in environmental education in New Zealand schools using a range of methods.

year: 2004

title: Māori and kava: New drug fashion or re-engagement with 'kawa'?

authors: Aporosa, S. 'Apo'

subjects: kava

abstract: The Pacific “cultural keystone species” (Garibaldi & Turner, 2004, pp.1,5) kava is uniquely linked with notions of mana (spiritual power) and used to infuse efficacy into celebrations and life events. The drink, made from this potent icon of identity, brings on feelings of relaxation without marked euphoria, aiding clear-minded talanoa (discussion) to facilitate important and/or contentious dialogue. The sharing and drinking of kava as part of host/guest formalities is used in bringing about mutuality and unity (Aporosa, 2019). It is estimated there are more than 20,000 kava users in Aotearoa/New Zealand on an average Friday or Saturday night, with increasing interest and uptake of this indigenous drink by Māori. Crowley (1994) reports that the Māori word ‘kawa’, literally meaning ‘marae protocol’, has its linguistic foundations in ‘kava’ and the practices associated with this cultural keystone species. This paper explores that linguistic union to ask whether increased kava use by Māori has greater significance than simply a new interaction with a foreign drug substance. Further, due to the use of kava in facilitating talanoa (discussion), the possibility of kava playing a similar role for Māori is considered, one linked to ancestral Pacific connections aimed at assisting kōrero (talanoa), cultural connectedness and practice.

text: Māori and kava: New drug fashion or re-engagement with 'kawa'? The Pacific “cultural keystone species” (Garibaldi & Turner, 2004, pp.1,5) kava is uniquely linked with notions of mana (spiritual power) and used to infuse efficacy into celebrations and life events. The drink, made from this potent icon of identity, brings on feelings of relaxation without marked euphoria, aiding clear-minded talanoa (discussion) to facilitate important and/or contentious dialogue. The sharing and drinking of kava as part of host/guest formalities is used in bringing about mutuality and unity (Aporosa, 2019). It is estimated there are more than 20,000 kava users in Aotearoa/New Zealand on an average Friday or Saturday night, with increasing interest and uptake of this indigenous drink by Māori. Crowley (1994) reports that the Māori word ‘kawa’, literally meaning ‘marae protocol’, has its linguistic foundations in ‘kava’ and the practices associated with this cultural keystone species. This paper explores that linguistic union to ask whether increased kava use by Māori has greater significance than simply a new interaction with a foreign drug substance. Further, due to the use of kava in facilitating talanoa (discussion), the possibility of kava playing a similar role for Māori is considered, one linked to ancestral Pacific connections aimed at assisting kōrero (talanoa), cultural connectedness and practice.

year: 2019