title: Body image amongst elite rugby union players.

authors: Gibson, Claire

abstract: There is limited information on the risk of eating disorders and body image of elite male athletes. However, research suggests there are some athletes who have poor body image and they may be at increased risk of developing eating disorders. Therefore, the current study investigated risk of eating disorders, body image, and the relationship with age, in elite rugby union players during their pre-season training period. This cross-sectional study was undertaken at the start of the pre-season amongst elite rugby union players in New Zealand. Twenty-six professional rugby union players completed a 49-item questionnaire on body image and disordered eating. A ‘body image score’ was calculated from questionnaire subscales including ‘drive for thinness’, ‘bulimia’ and ‘body dissatisfaction’, with total scores above twenty indicative of poor body image. Body image scores varied from 8-39 out of a possible 0-100. Disordered eating behaviours were reported, including binge eating at least once a week (15%, n=4/26), pathogenic weight control use (4%, n=1/26) and avoidance of certain foods (77%, n=20/26). There was a statistically significant inverse association between the bulimia subscale and age (P = 0.034). At the start of the pre-season training period, many elite rugby union players experience disturbances in body image. The prevalence of disordered eating behaviours is of concern, and needs to be minimised due to the negative impact on health and performance. A focus on assessment and education of younger male rugby players may be required in order to reduce disordered eating patterns.

text: Body image amongst elite rugby union players. There is limited information on the risk of eating disorders and body image of elite male athletes. However, research suggests there are some athletes who have poor body image and they may be at increased risk of developing eating disorders. Therefore, the current study investigated risk of eating disorders, body image, and the relationship with age, in elite rugby union players during their pre-season training period. This cross-sectional study was undertaken at the start of the pre-season amongst elite rugby union players in New Zealand. Twenty-six professional rugby union players completed a 49-item questionnaire on body image and disordered eating. A ‘body image score’ was calculated from questionnaire subscales including ‘drive for thinness’, ‘bulimia’ and ‘body dissatisfaction’, with total scores above twenty indicative of poor body image. Body image scores varied from 8-39 out of a possible 0-100. Disordered eating behaviours were reported, including binge eating at least once a week (15%, n=4/26), pathogenic weight control use (4%, n=1/26) and avoidance of certain foods (77%, n=20/26). There was a statistically significant inverse association between the bulimia subscale and age (P = 0.034). At the start of the pre-season training period, many elite rugby union players experience disturbances in body image. The prevalence of disordered eating behaviours is of concern, and needs to be minimised due to the negative impact on health and performance. A focus on assessment and education of younger male rugby players may be required in order to reduce disordered eating patterns.

year: 2017

title: Correlation-based feature selection for machine learning

authors: Hall, Mark A.

abstract: A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. 

CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB 1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. 

Further experiments compared CFS with a wrapper - a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets.

Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

text: Correlation-based feature selection for machine learning A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. 

CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB 1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. 

Further experiments compared CFS with a wrapper - a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets.

Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

year: 1999

title: Inter-propositional relations: An investigation of authentic Māori texts signalled in te reo Māori

authors: Whaanga, Hēmi

subjects: Māori manuscripts

abstract: I report here on a part of a research project involving the investigation of a range of authentic Māori texts in terms of two types of meaning relation - those that operate within propositions (referred to here as 'intra-propositional relations'), and those that link propositions or groups of propositions (referred to here as 'inter-propositional relations'). In focus here is the second of these types, that is, inter-propositional relations, and in particular, the ways in which they are signalled in te reo Māori. The implications of the findings for the teaching and learning of te reo Māori are also discussed.

text: Inter-propositional relations: An investigation of authentic Māori texts signalled in te reo Māori I report here on a part of a research project involving the investigation of a range of authentic Māori texts in terms of two types of meaning relation - those that operate within propositions (referred to here as 'intra-propositional relations'), and those that link propositions or groups of propositions (referred to here as 'inter-propositional relations'). In focus here is the second of these types, that is, inter-propositional relations, and in particular, the ways in which they are signalled in te reo Māori. The implications of the findings for the teaching and learning of te reo Māori are also discussed.

year: 2007-09