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: Soils of western Wright Valley, Antarctica

authors: McLeod, Malcolm

subjects: Dais

abstract: Western Wright Valley, from Wright Upper Glacier to the western end of the Dais, can be divided into three broad geomorphic regions: the elevated Labyrinth, the narrow Dais which is connected to the Labyrinth, and the North and South forks which are bifurcated by the Dais. Soil associations of Typic Haplorthels/Haploturbels with ice-cemented permafrost at < 70cm are most common in each of these geomorphic regions. Amongst the Haplo Great Groups are patches of Salic and Typic Anhyorthels with ice-cemented permafrost at > 70 cm. They are developed in situ in strongly weathered drift with very low surface boulder frequency and occur on the upper erosion surface of the Labyrinth and on the Dais. Typic Anhyorthels also occur at lower elevation on sinuous and patchy Wright Upper III drift within the forks. Salic Aquorthels exist only in the South Fork marginal to Don Juan Pond, whereas Salic Haplorthels occur in low areas of both South and North forks where any water table is> 50 cm. Most soils within the study area have an alkaline pH dominated by Na+ and Cl- ions. The low salt accumulation within Haplorthels/Haploturbels may be due to limited depth of soil development and possibly leaching.

text: Soils of western Wright Valley, Antarctica Western Wright Valley, from Wright Upper Glacier to the western end of the Dais, can be divided into three broad geomorphic regions: the elevated Labyrinth, the narrow Dais which is connected to the Labyrinth, and the North and South forks which are bifurcated by the Dais. Soil associations of Typic Haplorthels/Haploturbels with ice-cemented permafrost at < 70cm are most common in each of these geomorphic regions. Amongst the Haplo Great Groups are patches of Salic and Typic Anhyorthels with ice-cemented permafrost at > 70 cm. They are developed in situ in strongly weathered drift with very low surface boulder frequency and occur on the upper erosion surface of the Labyrinth and on the Dais. Typic Anhyorthels also occur at lower elevation on sinuous and patchy Wright Upper III drift within the forks. Salic Aquorthels exist only in the South Fork marginal to Don Juan Pond, whereas Salic Haplorthels occur in low areas of both South and North forks where any water table is> 50 cm. Most soils within the study area have an alkaline pH dominated by Na+ and Cl- ions. The low salt accumulation within Haplorthels/Haploturbels may be due to limited depth of soil development and possibly leaching.

year: 2009

title: Māori loanwords: a corpus of New Zealand English tweets

authors: Trye, David

abstract: Māori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the Māori community. Motivated by the lack of linguistic resources for studying how Māori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected Māori words that are likely to be known by New Zealanders who do not speak Māori. Since over 30% of these words turned out to be irrelevant (e.g., mana is a popular gaming term, Moana is a character from a Disney movie), we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.

text: Māori loanwords: a corpus of New Zealand English tweets Māori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the Māori community. Motivated by the lack of linguistic resources for studying how Māori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected Māori words that are likely to be known by New Zealanders who do not speak Māori. Since over 30% of these words turned out to be irrelevant (e.g., mana is a popular gaming term, Moana is a character from a Disney movie), we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.

year: 2019