title: Analyzing and repairing concept drift adaptation in data stream classification

authors: Halstead, Ben

subjects: computer science

abstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.

text: Analyzing and repairing concept drift adaptation in data stream classification Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.

year: 2021

title: Does investor sentiment predict the near-term returns of the Chinese stock market?

authors: Cheema, Muhammad A.

abstract: Recent evidence on the relationship between investor sentiment and subsequent monthly market returns in China shows that investor sentiment is a reliable momentum predictor since an increase (decrease) in investor sentiment leads to higher (lower) future returns. However, we suggest that momentum predictability of investor sentiment originates from the boom and bust period of 2006-2008 (the bubble period hereafter). The bubble period is characterized by several months of sustained optimism followed by several months of sustained pessimism, with the market consequently earning high (low) returns following high (low) sentiment months. Therefore, we find a strong positive association between investor sentiment and subsequent market returns during the bubble period. However, investor sentiment has a negligible impact on subsequent monthly market returns once we exclude the bubble period.

text: Does investor sentiment predict the near-term returns of the Chinese stock market? Recent evidence on the relationship between investor sentiment and subsequent monthly market returns in China shows that investor sentiment is a reliable momentum predictor since an increase (decrease) in investor sentiment leads to higher (lower) future returns. However, we suggest that momentum predictability of investor sentiment originates from the boom and bust period of 2006-2008 (the bubble period hereafter). The bubble period is characterized by several months of sustained optimism followed by several months of sustained pessimism, with the market consequently earning high (low) returns following high (low) sentiment months. Therefore, we find a strong positive association between investor sentiment and subsequent market returns during the bubble period. However, investor sentiment has a negligible impact on subsequent monthly market returns once we exclude the bubble period.

year: 2018

title: Te Rau Puawai 2002-2004: An evaluation

authors: Nikora, Linda Waimarie

subjects: evaluation

abstract: Established in 1999 as a joint workforce initiative between the former Health Funding Authority and Massey University, Te Rau Puawai aimed to support 100 Maori students to graduate with mental health qualifications within a five year period. The goal of Te Rau Puawai is to contribute to the building of capacity for Maori in the mental health workforce. The programme exceeded its performance expectations in the first two years (1999-2001) with 56 bursars completing their qualifications. Bursars achieved an 80% pass rate compared with 65% for all students at Massey University as a whole. In 2004, this pass rate has continued, a significant achievement in light of increasing numbers of bursars being accepted and many without previously studying at the tertiary level.

In 2001 the Maori & Psychology Research Unit (MPRU) at the University of Waikato undertook an evaluation of Te Rau Puawai reporting on the programme's success and identifying any barriers the programme needed to address. The 2002 evaluation report outlines critical success factors and recommendations for improvement. In 2003 Te Rau Puawai negotiated further funding from the Mental Health Directorate (MeHD) of the Ministry of Health under the Mental Health Workforce Development Strategy (2002). Workforce development is critical in building capacity and capability in the mental health workforce to increase appropriately skilled workers required to meet the mental health needs of Maori communities. In 2004 the Ministry of Health requested a follow-up evaluation to provide a descriptive record of programme activities and progress from April 2002 to December 2004. This report provides an overview of Te Rau Puawai activities between 2002 and 2004; the progress and contributions made by bursars to the mental health workforce; and a reassessment of the programme's critical success factors.

text: Te Rau Puawai 2002-2004: An evaluation Established in 1999 as a joint workforce initiative between the former Health Funding Authority and Massey University, Te Rau Puawai aimed to support 100 Maori students to graduate with mental health qualifications within a five year period. The goal of Te Rau Puawai is to contribute to the building of capacity for Maori in the mental health workforce. The programme exceeded its performance expectations in the first two years (1999-2001) with 56 bursars completing their qualifications. Bursars achieved an 80% pass rate compared with 65% for all students at Massey University as a whole. In 2004, this pass rate has continued, a significant achievement in light of increasing numbers of bursars being accepted and many without previously studying at the tertiary level.

In 2001 the Maori & Psychology Research Unit (MPRU) at the University of Waikato undertook an evaluation of Te Rau Puawai reporting on the programme's success and identifying any barriers the programme needed to address. The 2002 evaluation report outlines critical success factors and recommendations for improvement. In 2003 Te Rau Puawai negotiated further funding from the Mental Health Directorate (MeHD) of the Ministry of Health under the Mental Health Workforce Development Strategy (2002). Workforce development is critical in building capacity and capability in the mental health workforce to increase appropriately skilled workers required to meet the mental health needs of Maori communities. In 2004 the Ministry of Health requested a follow-up evaluation to provide a descriptive record of programme activities and progress from April 2002 to December 2004. This report provides an overview of Te Rau Puawai activities between 2002 and 2004; the progress and contributions made by bursars to the mental health workforce; and a reassessment of the programme's critical success factors.

year: 2005-01-01