title: Making better use of global discretization

authors: Frank, Eibe

subjects: computer science

abstract: Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior discretization is essential. Even if it can, prior discretization often accelerates induction, and may produce simpler and more accurate classifiers. 
As it is generally done, global discretization denies the learning algorithm any chance of taking advantage of the ordering information implicit in numeric attributes. However, a simple transformation of discretized data preserves this information in a form that learners can use. We show that, compared to using the discretized data directly, this transformation significantly increases the accuracy of decision trees built by C4.5, decision lists built by PART, and decision tables built using the wrapper method, on several bench-mark datasets. Moreover, it can significantly reduce the size of the resulting classifiers.
This simple technique makes global discretization an even more useful tool for data preprocessing

text: Making better use of global discretization Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior discretization is essential. Even if it can, prior discretization often accelerates induction, and may produce simpler and more accurate classifiers. 
As it is generally done, global discretization denies the learning algorithm any chance of taking advantage of the ordering information implicit in numeric attributes. However, a simple transformation of discretized data preserves this information in a form that learners can use. We show that, compared to using the discretized data directly, this transformation significantly increases the accuracy of decision trees built by C4.5, decision lists built by PART, and decision tables built using the wrapper method, on several bench-mark datasets. Moreover, it can significantly reduce the size of the resulting classifiers.
This simple technique makes global discretization an even more useful tool for data preprocessing

year: 1999

title: Does invasiveness predict competition outcomes between ecologically similar invasive blowflies?

authors: Nunn, Daniel

abstract: Biological invasion is a complex puzzle, and its significance is at an all-time high. Current research highlights that the success of invasive species is partly due to their superior performance in key behavioural traits, such as aggression and boldness, especially when compared to ecologically similar native species. However, less attention has been given to differences in these traits among invasive species themselves. Not all invaders are created equal; they vary in their degree of invasiveness, often characterised by the extent of their global spread. If behavioural traits are crucial for invasiveness, it stands to reason that more invasive species - those with greater success - should outperform less invasive ones.

I tested this hypothesis using larval competition between differentially invasive blowfly species. Calliphora stygia has a limited distribution, found only in Australia and New Zealand, whereas Lucilia sericata and Lucilia cuprina (Lucilia spp.) are invaders with almost global distributions. Under direct laboratory competition, the highly invasive Lucilia spp. exhibited fewer body size reductions than C. stygia, regardless of intra- or interspecific competition levels. These findings suggest that competitive traits may exist along a continuum from native to invasive species, increasing predictably with invasiveness. This insight highlights invasiveness as a potentially reliable metric for predicting competition outcomes between ecologically similar sympatric invaders.

text: Does invasiveness predict competition outcomes between ecologically similar invasive blowflies? Biological invasion is a complex puzzle, and its significance is at an all-time high. Current research highlights that the success of invasive species is partly due to their superior performance in key behavioural traits, such as aggression and boldness, especially when compared to ecologically similar native species. However, less attention has been given to differences in these traits among invasive species themselves. Not all invaders are created equal; they vary in their degree of invasiveness, often characterised by the extent of their global spread. If behavioural traits are crucial for invasiveness, it stands to reason that more invasive species - those with greater success - should outperform less invasive ones.

I tested this hypothesis using larval competition between differentially invasive blowfly species. Calliphora stygia has a limited distribution, found only in Australia and New Zealand, whereas Lucilia sericata and Lucilia cuprina (Lucilia spp.) are invaders with almost global distributions. Under direct laboratory competition, the highly invasive Lucilia spp. exhibited fewer body size reductions than C. stygia, regardless of intra- or interspecific competition levels. These findings suggest that competitive traits may exist along a continuum from native to invasive species, increasing predictably with invasiveness. This insight highlights invasiveness as a potentially reliable metric for predicting competition outcomes between ecologically similar sympatric invaders.

year: 2024

title: How Maui captured the sun: using a MUD for educational simulation

authors: Cunningham, Sally Jo

subjects: computer science

abstract: MUDs (Multi-User Dungeons) are text-based, multi-user communication and modelling programs. This paper investigates the potential of a popular extensible MUD, the LambdaMOO system, as a tool for second language training and for educational simulation gaming.

text: How Maui captured the sun: using a MUD for educational simulation MUDs (Multi-User Dungeons) are text-based, multi-user communication and modelling programs. This paper investigates the potential of a popular extensible MUD, the LambdaMOO system, as a tool for second language training and for educational simulation gaming.

year: 1995-12