title: Generating rule sets from model trees

authors: Holmes, Geoffrey

subjects: science & technology

abstract: Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of If-Then rules—have the potential to be more compact and therefore more understandable than their tree counterparts.

We present an algorithm for inducing simple, accurate decision lists from model trees. Model trees are built repeatedly and the best rule is selected at each iteration. This method produces rule sets that are as accurate but smaller than the model tree constructed from the entire dataset. Experimental results for various heuristics which attempt to find a compromise between rule accuracy and rule coverage are reported. We show that our method produces comparably accurate and smaller rule sets than the commercial state-of-the-art rule learning system Cubist.

text: Generating rule sets from model trees Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of If-Then rules—have the potential to be more compact and therefore more understandable than their tree counterparts.

We present an algorithm for inducing simple, accurate decision lists from model trees. Model trees are built repeatedly and the best rule is selected at each iteration. This method produces rule sets that are as accurate but smaller than the model tree constructed from the entire dataset. Experimental results for various heuristics which attempt to find a compromise between rule accuracy and rule coverage are reported. We show that our method produces comparably accurate and smaller rule sets than the commercial state-of-the-art rule learning system Cubist.

year: 1999

title: Text categorization using compression models

authors: Frank, Eibe

subjects: computer science

abstract: Text categorization, or the assignment of natural language texts to predefined categories based on their content, is of growing importance as the volume of information available on the internet continues to overwhelm us. The use of predefined categories implies a “supervised learning” approach to categorization, where already-classified articles which effectively define the categories are used as “training data” to build a model that can be used for classifying new articles that comprise the “test data”. This contrasts with “unsupervised” learning, where there is no training data and clusters of like documents are sought amongst the test articles. With supervised learning, meaningful labels (such as keyphrases) are attached to the training documents, and appropriate labels can be assigned automatically to test documents depending on which category they fall into.

text: Text categorization using compression models Text categorization, or the assignment of natural language texts to predefined categories based on their content, is of growing importance as the volume of information available on the internet continues to overwhelm us. The use of predefined categories implies a “supervised learning” approach to categorization, where already-classified articles which effectively define the categories are used as “training data” to build a model that can be used for classifying new articles that comprise the “test data”. This contrasts with “unsupervised” learning, where there is no training data and clusters of like documents are sought amongst the test articles. With supervised learning, meaningful labels (such as keyphrases) are attached to the training documents, and appropriate labels can be assigned automatically to test documents depending on which category they fall into.

year: 2000-01

title: Observations by and conversations with health workers and hospital personnel involved in transferring Maori patients and whānau to Waikato Hospital in Aotearoa New Zealand

authors: Masters-Awatere, Bridgette

subjects: Science & Technology

abstract: The predominant focus of Aotearoa New Zealand’s public health system on biomedical models of health has left little room for meaningful engagement with holistic indigenous approaches. Culturally appropriate provision and support are recognized for their relevance and importance during hospital transferals. Hospital staff involved in transfers to one of New Zealand’s trauma centers share their observations of whānau Māori engagement during an admission away from their home base. Sixteen key informants share their experiences, which are presented as strategies and challenges to whānau engagement. Three main themes highlight challenges within the health system that make it difficult for hospital staff to engage whānau in the desired ways and as often as both parties would like. Key informants described services and practices that are not designed with patients and their whānau in mind; instead they are designed by clinicians around the needs of administrative systems. As employees within the public health system, key informants felt powerless to challenge dominant settings. Nevertheless, employees managed to circumnavigate processes. Our findings highlight the need for continued decolonization and anti-racism work within public health settings.

text: Observations by and conversations with health workers and hospital personnel involved in transferring Maori patients and whānau to Waikato Hospital in Aotearoa New Zealand The predominant focus of Aotearoa New Zealand’s public health system on biomedical models of health has left little room for meaningful engagement with holistic indigenous approaches. Culturally appropriate provision and support are recognized for their relevance and importance during hospital transferals. Hospital staff involved in transfers to one of New Zealand’s trauma centers share their observations of whānau Māori engagement during an admission away from their home base. Sixteen key informants share their experiences, which are presented as strategies and challenges to whānau engagement. Three main themes highlight challenges within the health system that make it difficult for hospital staff to engage whānau in the desired ways and as often as both parties would like. Key informants described services and practices that are not designed with patients and their whānau in mind; instead they are designed by clinicians around the needs of administrative systems. As employees within the public health system, key informants felt powerless to challenge dominant settings. Nevertheless, employees managed to circumnavigate processes. Our findings highlight the need for continued decolonization and anti-racism work within public health settings.

year: 2020