title: SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams

authors: Sun, Yibin

abstract: Most research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention. This paper proposes Self-Optimising K-Nearest Leaves (SOKNL), a novel forest-based algorithm for streaming regression problems. Specifically, the Adaptive Random Forest Regression, a state-of-the-art online regression algorithm is extended like this: in each leaf, a representative data point – also called centroid – is generated by compressing the information from all instances in that leaf. During the prediction step, instead of letting all trees in the forest participate, the distances between the input instance and all centroids from relevant leaves are calculated, only k trees that possess the smallest distances are utilised for the prediction. Furthermore, we simplify the algorithm by introducing a mechanism for tuning the k values, which is dynamically and automatically optimised based on historical information. This new algorithm produces promising predictive results and achieves a superior ranking according to statistical testing when compared with several standard stream regression methods over typical benchmark datasets. This improvement incurs only a small increase in runtime and memory consumption over the basic Adaptive Random Forest Regressor.

text: SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams Most research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention. This paper proposes Self-Optimising K-Nearest Leaves (SOKNL), a novel forest-based algorithm for streaming regression problems. Specifically, the Adaptive Random Forest Regression, a state-of-the-art online regression algorithm is extended like this: in each leaf, a representative data point – also called centroid – is generated by compressing the information from all instances in that leaf. During the prediction step, instead of letting all trees in the forest participate, the distances between the input instance and all centroids from relevant leaves are calculated, only k trees that possess the smallest distances are utilised for the prediction. Furthermore, we simplify the algorithm by introducing a mechanism for tuning the k values, which is dynamically and automatically optimised based on historical information. This new algorithm produces promising predictive results and achieves a superior ranking according to statistical testing when compared with several standard stream regression methods over typical benchmark datasets. This improvement incurs only a small increase in runtime and memory consumption over the basic Adaptive Random Forest Regressor.

year: 2022-09-01

title: Wage Structures and Employment Outcomes in New Zealand, and Their Relationship to Technological Change

authors: Hector, Christopher James

subjects: Inequality-New Zealand

abstract: After 100 years at an historically low level, inequality began to rise in the late 20th century, a trend which was especially marked in the English-speaking countries including New Zealand. Various explanations have been advanced, but internationally the most favoured theory is skill-biased technological change, driven by the new information and communication technologies. This thesis used income and wage data from the New Zealand Population Census and the New Zealand Income Survey to examine wage trends between 1991 and 2004. As in other developed countries wage dispersion was increasing in the 1990s, though it appears to have slowed since 2001, and the increased inequality is strongly correlated with workers' skills and qualifications. There is also a correlation between new technology and earnings inequality, but this appears to be attributable to the demand for skills in the industries which are changing fastest, rather than anything intrinsic to the new technology.

text: Wage Structures and Employment Outcomes in New Zealand, and Their Relationship to Technological Change After 100 years at an historically low level, inequality began to rise in the late 20th century, a trend which was especially marked in the English-speaking countries including New Zealand. Various explanations have been advanced, but internationally the most favoured theory is skill-biased technological change, driven by the new information and communication technologies. This thesis used income and wage data from the New Zealand Population Census and the New Zealand Income Survey to examine wage trends between 1991 and 2004. As in other developed countries wage dispersion was increasing in the 1990s, though it appears to have slowed since 2001, and the increased inequality is strongly correlated with workers' skills and qualifications. There is also a correlation between new technology and earnings inequality, but this appears to be attributable to the demand for skills in the industries which are changing fastest, rather than anything intrinsic to the new technology.

year: 2007

title: Ahumai

authors: Temara, Pou

subjects: Pātere

abstract: E tika ana kia punarua te wānanga i ēnei pātere e rua nā te mea he mārama te kite iho o whatu he ōrite ētahi o ngā rārangi kupu, ngā whakaaro me ngā kareāroto ki te pātere a Ahumai. Ahakoa kāore i te takoto te ingoa o te kaitito i tēnei pātere, nā te ōrite o ētahi o ngā kupu, e taea te whakapae, nā te ringa kotahi ēnei pātere i waituhi? Kai roto hoki i te pātere nei e takaoto ana ngā rohe o Raukawa pērā i Wharepūhunga, me ngā tangata whai mana o tērā iwi pērā i a Tīkao. Hei taituarā ka hahua ake anō te kōrero a Turanga Te Mauparaoa ki te tata o te rohe o te iwi nāna te waiata nei, ki tōna rohe. Kāore te Māori e kī ‘nō tua ake nei tēnei pātere’ mēnā he tawhiti atu te iwi i tōna. Nō reira, ki taku wānanga, ehara tēnei pātere nō ngā iwi o Taranaki. He tawhiti rawa a Taranaki i Murupara. Hāunga a Raukawa, koia te iwi kai ‘tua ake nei’.

E tuku ana ahau i taku ihu kia puta. Ki te tapahia mai, e pai ana. E kī ana hoki au, ko te mea poto te titonga tuatahi, ko te mea roa te titonga o muri mai. He ariā te mea tuatahi nō te mea tuarua.

text: Ahumai E tika ana kia punarua te wānanga i ēnei pātere e rua nā te mea he mārama te kite iho o whatu he ōrite ētahi o ngā rārangi kupu, ngā whakaaro me ngā kareāroto ki te pātere a Ahumai. Ahakoa kāore i te takoto te ingoa o te kaitito i tēnei pātere, nā te ōrite o ētahi o ngā kupu, e taea te whakapae, nā te ringa kotahi ēnei pātere i waituhi? Kai roto hoki i te pātere nei e takaoto ana ngā rohe o Raukawa pērā i Wharepūhunga, me ngā tangata whai mana o tērā iwi pērā i a Tīkao. Hei taituarā ka hahua ake anō te kōrero a Turanga Te Mauparaoa ki te tata o te rohe o te iwi nāna te waiata nei, ki tōna rohe. Kāore te Māori e kī ‘nō tua ake nei tēnei pātere’ mēnā he tawhiti atu te iwi i tōna. Nō reira, ki taku wānanga, ehara tēnei pātere nō ngā iwi o Taranaki. He tawhiti rawa a Taranaki i Murupara. Hāunga a Raukawa, koia te iwi kai ‘tua ake nei’.

E tuku ana ahau i taku ihu kia puta. Ki te tapahia mai, e pai ana. E kī ana hoki au, ko te mea poto te titonga tuatahi, ko te mea roa te titonga o muri mai. He ariā te mea tuatahi nō te mea tuarua.

year: 2017