title: Practical feature subset selection for machine learning

authors: Hall, Mark A.

subjects: computer science

abstract: Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data  that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithms

text: Practical feature subset selection for machine learning Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data  that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithms

year: 1998

title: Two faiths, one banner: when Muslims marched with Christians across Europe’s battlegrounds [Book review]

authors: Pratt, Douglas

subjects: book review

abstract: This article reviews the book “Two faiths, one banner: when Muslims marched with Christians across Europe’s battlegrounds”, by Ian Almond.

text: Two faiths, one banner: when Muslims marched with Christians across Europe’s battlegrounds [Book review] This article reviews the book “Two faiths, one banner: when Muslims marched with Christians across Europe’s battlegrounds”, by Ian Almond.

year: 2010-10

title: Mana motuhake ā-raraunga: datafication and social science research in Aotearoa

authors: Kukutai, Tahu

abstract: Social practices, interactions and relations are increasingly being turned into data, driven by technologies that enable new methods of data accumulation, digitisation, integration and manipulation (Taylor and Broeders 2015; Couldry and Yu 2018; Mann and Daly 2019). This ‘datafication’ is not only transforming social relations, it is also reshaping social science through the generation of datasets that open up new avenues for research and methodological development (van Dijck 2014) and generating debate about the relationships between data, digitalisation, ethics and human wellbeing (boyd and Crawford 2012; Mittelstadt and Floridi 2016; O’Neil 2016; Eubanks 2018).

text: Mana motuhake ā-raraunga: datafication and social science research in Aotearoa Social practices, interactions and relations are increasingly being turned into data, driven by technologies that enable new methods of data accumulation, digitisation, integration and manipulation (Taylor and Broeders 2015; Couldry and Yu 2018; Mann and Daly 2019). This ‘datafication’ is not only transforming social relations, it is also reshaping social science through the generation of datasets that open up new avenues for research and methodological development (van Dijck 2014) and generating debate about the relationships between data, digitalisation, ethics and human wellbeing (boyd and Crawford 2012; Mittelstadt and Floridi 2016; O’Neil 2016; Eubanks 2018).

year: 2019