title: The online performance estimation framework: heterogeneous ensemble learning for data streams

authors: van Rijn, Jan N.

subjects: Science & Technology

abstract: Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, comprised of fundamentally different model types. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. We study the use of heterogeneous ensembles for data streams. We introduce the Online Performance Estimation framework, which dynamically weights the votes of individual classifiers in an ensemble. Using an internal evaluation on recent training data, it measures how well ensemble members performed on this and dynamically updates their weights. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging, while being significantly faster. All experimental results from this work are easily reproducible and publicly available online.

text: The online performance estimation framework: heterogeneous ensemble learning for data streams Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, comprised of fundamentally different model types. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. We study the use of heterogeneous ensembles for data streams. We introduce the Online Performance Estimation framework, which dynamically weights the votes of individual classifiers in an ensemble. Using an internal evaluation on recent training data, it measures how well ensemble members performed on this and dynamically updates their weights. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging, while being significantly faster. All experimental results from this work are easily reproducible and publicly available online.

year: 2018

title: Lossless document image compression

authors: Inglis, Stuart J.

abstract: Document image compression reduces the storage requirements for digitised books or documents by using characters as the fundamental unit of compression. Compression gains can be achieved by identifying regions that contain text, isolating unique characters, and storing them in a codebook.

This thesis investigates several fundamental areas of the compression process. Algorithms for each area are tested on a corpus of images and the improvements tested for statistical significance. Methods for isolating characters from a bitmap are investigated along with techniques for determining reading order. We introduce the use of the docstrum to aid image compression and show that it improves upon previous methods. The Hough transform is shown to be an accurate method for determining page skew and gives robust results over a range of image resolutions. Compression is shown to improve when the skew of an image is determined automatically, and used to determine reading order.

If images can be segmented into regions of text and non-text, it is possible to change the compression model to reflect the region’s contents. Instead of developing ad hoc methods to classify the regions, we introduce the use of machine learning schemes, and show both high predictive accuracy and compression improvements. Machine learning schemes are also applied to the problem of matching characters to determine similarity. Using the scheme’s choice of parameters, the matching methods and their respective compression gains can be compared fairly. We investigate the changes in codebook size and compression gains when compressing multiple pages.

The compression system presented is complete, free of arbitrarily introduced parameters and achieves the best known lossless compression on a public corpus of images.

text: Lossless document image compression Document image compression reduces the storage requirements for digitised books or documents by using characters as the fundamental unit of compression. Compression gains can be achieved by identifying regions that contain text, isolating unique characters, and storing them in a codebook.

This thesis investigates several fundamental areas of the compression process. Algorithms for each area are tested on a corpus of images and the improvements tested for statistical significance. Methods for isolating characters from a bitmap are investigated along with techniques for determining reading order. We introduce the use of the docstrum to aid image compression and show that it improves upon previous methods. The Hough transform is shown to be an accurate method for determining page skew and gives robust results over a range of image resolutions. Compression is shown to improve when the skew of an image is determined automatically, and used to determine reading order.

If images can be segmented into regions of text and non-text, it is possible to change the compression model to reflect the region’s contents. Instead of developing ad hoc methods to classify the regions, we introduce the use of machine learning schemes, and show both high predictive accuracy and compression improvements. Machine learning schemes are also applied to the problem of matching characters to determine similarity. Using the scheme’s choice of parameters, the matching methods and their respective compression gains can be compared fairly. We investigate the changes in codebook size and compression gains when compressing multiple pages.

The compression system presented is complete, free of arbitrarily introduced parameters and achieves the best known lossless compression on a public corpus of images.

year: 1999

title: Hōmai te Waiora ki Ahau: te ara whakamua - towards the establishment of construct validity

authors: Palmer, Stephanie

abstract: Hōmai te Waiora ki Ahau: te ara
whakamua, is about the development of a
tool to measure psychological wellbeing
among Māori. Why is it relevant? Because
a quick look at the June 2002 edition of
New Zealand’s Journal of Psychology will
show you that the wellbeing measures
being used in this country are not responsive to the needs of
Māori, are not based on Māori concepts or
Constructs, do not facilitate Māori participation in
te ao Māori and do not provide pathways through
which Māori can develop a positive Māori
identity.
It is highly unlikely that the tools
which psychologists use to measure
wellbeing among Māori will help Māori to
experience whānau ora and that, as we all
know, is the paramount health objective for
Māori (Ministry of Health, 2002). In this
regard, it would seem that the powers that
be in psychology are failing to meet their
Treaty obligations to Māori. Therefore,
Hōmai te Waiora ki Ahau simply aims to assist change. This presentation will briefly
describe the context of the development of
this measure, the methodologies used to
develop this tool, the outcomes of a small
pilot-study, and current challenges and
future directions for Hōmai te Waiora ki
Ahau.

text: Hōmai te Waiora ki Ahau: te ara whakamua - towards the establishment of construct validity Hōmai te Waiora ki Ahau: te ara
whakamua, is about the development of a
tool to measure psychological wellbeing
among Māori. Why is it relevant? Because
a quick look at the June 2002 edition of
New Zealand’s Journal of Psychology will
show you that the wellbeing measures
being used in this country are not responsive to the needs of
Māori, are not based on Māori concepts or
Constructs, do not facilitate Māori participation in
te ao Māori and do not provide pathways through
which Māori can develop a positive Māori
identity.
It is highly unlikely that the tools
which psychologists use to measure
wellbeing among Māori will help Māori to
experience whānau ora and that, as we all
know, is the paramount health objective for
Māori (Ministry of Health, 2002). In this
regard, it would seem that the powers that
be in psychology are failing to meet their
Treaty obligations to Māori. Therefore,
Hōmai te Waiora ki Ahau simply aims to assist change. This presentation will briefly
describe the context of the development of
this measure, the methodologies used to
develop this tool, the outcomes of a small
pilot-study, and current challenges and
future directions for Hōmai te Waiora ki
Ahau.

year: 2003