title: Aging and rejuvenating strategies for fading windows in multi-label classification on data streams

authors: Roseberry, Martha

subjects: computer science

abstract: Combining the challenges of streaming data and multi-label learning, the task of mining a drifting, multi-label data stream requires methods that can accurately predict label sets, adapt to various types of concept drift, and run fast enough to process each data point before the next arrives. To achieve greater accuracy, many multi-label algorithms use computationally expensive techniques, such as multiple adaptive windows, with little concern for runtime and memory complexity. We present Aging and Rejuvenating kNN (ARkNN), which uses simple resources and efficient strategies to weight instances based on age, predictive performance, and similarity to the incoming data. We break down ARkNN into its component strategies to show the impact of each and experimentally compare ARkNN to seven state-of-the-art methods for learning from multi-label data streams. We demonstrate that it is possible to achieve competitive performance in multi-label classification on streams without sacrificing runtime and memory use, and without using complex and computationally expensive dual memory strategies.

text: Aging and rejuvenating strategies for fading windows in multi-label classification on data streams Combining the challenges of streaming data and multi-label learning, the task of mining a drifting, multi-label data stream requires methods that can accurately predict label sets, adapt to various types of concept drift, and run fast enough to process each data point before the next arrives. To achieve greater accuracy, many multi-label algorithms use computationally expensive techniques, such as multiple adaptive windows, with little concern for runtime and memory complexity. We present Aging and Rejuvenating kNN (ARkNN), which uses simple resources and efficient strategies to weight instances based on age, predictive performance, and similarity to the incoming data. We break down ARkNN into its component strategies to show the impact of each and experimentally compare ARkNN to seven state-of-the-art methods for learning from multi-label data streams. We demonstrate that it is possible to achieve competitive performance in multi-label classification on streams without sacrificing runtime and memory use, and without using complex and computationally expensive dual memory strategies.

year: 2023-03-27

title: Incorporating discontinuous preferences into the analysis of discrete choice experiments

authors: Campbell, Danny

subjects: discontinuous preferences

abstract: Data from a discrete choice experiment on improvements of rural landscape attributes are used to investigate the implications of discontinuous preferences on willingness to pay estimates. Using a multinomial error component logit model, we explore differences in scale and unexplained variance between respondents with discontinuous and continuous preferences and condition taste intensities on whether or not each attribute was considered by the respondent during the evaluation of alternatives. Results suggest that significant improvements in model performance can be achieved when discontinuous preferences are accommodated in the econometric specification, and that the magnitude and robustness of the willingness to pay estimates are sensitive to discontinuous preferences.

text: Incorporating discontinuous preferences into the analysis of discrete choice experiments Data from a discrete choice experiment on improvements of rural landscape attributes are used to investigate the implications of discontinuous preferences on willingness to pay estimates. Using a multinomial error component logit model, we explore differences in scale and unexplained variance between respondents with discontinuous and continuous preferences and condition taste intensities on whether or not each attribute was considered by the respondent during the evaluation of alternatives. Results suggest that significant improvements in model performance can be achieved when discontinuous preferences are accommodated in the econometric specification, and that the magnitude and robustness of the willingness to pay estimates are sensitive to discontinuous preferences.

year: 2007-09

title: “If it don’t talk, it's not whakapapa data”: Conceptualising whakapapa data in a digital age

authors: Pēpi Tarapa‐Dewes, Ella

subjects: data protection

abstract: In Te Ao Māori, whakapapa is a sacred framework that connects all aspects of existence. Whakapapa is highly sensitive information and a taonga that requires active protection. But what can be considered whakapapa data in a digital age where data linkage is ubiquitous and the boundaries between personal and collective information are increasingly blurred? We explore this question through a case study with Ngāti Tiipa, a hapū that is actively engaged in its own data sovereignty research. Reflexive thematic analysis of whānau kōrero identified three types of whakapapa data, each denoting a specific kind of connection: 1) between relatives, both living and deceased; 2) between people and places of cultural significance; and 3) between people and the spiritual realm. Whānau concepts of whakapapa data tend to be context‐specific, suggesting that it is more useful to think of whakapapa data as dynamic and relational rather than a fixed, inherent attribute.

text: “If it don’t talk, it's not whakapapa data”: Conceptualising whakapapa data in a digital age In Te Ao Māori, whakapapa is a sacred framework that connects all aspects of existence. Whakapapa is highly sensitive information and a taonga that requires active protection. But what can be considered whakapapa data in a digital age where data linkage is ubiquitous and the boundaries between personal and collective information are increasingly blurred? We explore this question through a case study with Ngāti Tiipa, a hapū that is actively engaged in its own data sovereignty research. Reflexive thematic analysis of whānau kōrero identified three types of whakapapa data, each denoting a specific kind of connection: 1) between relatives, both living and deceased; 2) between people and places of cultural significance; and 3) between people and the spiritual realm. Whānau concepts of whakapapa data tend to be context‐specific, suggesting that it is more useful to think of whakapapa data as dynamic and relational rather than a fixed, inherent attribute.

year: 2026-02