title: An entropy gain measure of numeric prediction performance

authors: Trigg, Leonard E.

subjects: computer science

abstract: Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

text: An entropy gain measure of numeric prediction performance Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

year: 1998-05

title: Biotechnology's wheel of knowledge

authors: Buntting, Catherine Michelle

abstract: Article with results of study carried out to raise awareness of the role of biotechnology in the New Zealand curriculum. The findings highlighted that biotechnology learning needs to be situated in real life contexts that are relevant to the leaner.

text: Biotechnology's wheel of knowledge Article with results of study carried out to raise awareness of the role of biotechnology in the New Zealand curriculum. The findings highlighted that biotechnology learning needs to be situated in real life contexts that are relevant to the leaner.

year: 2006

title: Human dimensions of global environmental change: A review of frameworks and research gaps.

authors: Urich, Peter

subjects: global environmental change

abstract: This review of current knowledge in, and development of, priorities for research into the human dimensions of global environmental change has been conducted to assist the Foundation for Research Science and Technology to formulate their research needs agenda. An overview of the emergence of the human dimension of the global environmental change research field is used as a prelude to a review of the international literature on frameworks for conducting human dimensions research. The terminology used in the human dimension field is diverse. An attempt is therefore made to consolidate the myriad of definitions for key concepts, in order to form a basis for human dimensions research in New Zealand and build a platform for the remainder of this report. Three disciplinary areas of academic inquiry were explored in the context of human dimensions of global environmental change research: economics, law and biological sciences. These disciplinary perspectives are reviewed through the international literature and in a New Zealand context. A conceptual framework for research into the human dimensions of global environmental change is provided based on the knowledge-base developed from the both international and New Zealand research. Core concepts, such as found in multidisciplinary and transdisciplinary research are reviewed in historical context to form the basis for a specific research framework for New Zealand. Some general needs for research are derived from the New Zealand framework. The remainder of this report details the methods used for drawing out expert opinion on the priorities for research on the human dimensions of global environmental change in the context of information, knowledge and method. Thirty-eight interviews and two hui were conducted and 254 research needs defined by questions were obtained. A modified Delphi technique was applied to the questions and prioritised lists of responses are provided. The final list
Provides European/Pakeha and Māori perspectives. Divergence and convergence between the European/Pakeha and Māori responses is provided, as well as justification for the prioritisation stance that is taken. This research had four outputs: A conceptual framework for considering the human dimensions of global environmental changes and research contributions; A description of the gaps in knowledge that impede effective response to global change and provisional research needs: one for New Zealand generally, and one on Māori in particular; A network of end-users and research providers that are cognisant of, and contributors to, the human dimensions research assessment; A set of priority research needs pertaining to the human dimensions of global environmental change, in a form suitable for use by FRST.

text: Human dimensions of global environmental change: A review of frameworks and research gaps. This review of current knowledge in, and development of, priorities for research into the human dimensions of global environmental change has been conducted to assist the Foundation for Research Science and Technology to formulate their research needs agenda. An overview of the emergence of the human dimension of the global environmental change research field is used as a prelude to a review of the international literature on frameworks for conducting human dimensions research. The terminology used in the human dimension field is diverse. An attempt is therefore made to consolidate the myriad of definitions for key concepts, in order to form a basis for human dimensions research in New Zealand and build a platform for the remainder of this report. Three disciplinary areas of academic inquiry were explored in the context of human dimensions of global environmental change research: economics, law and biological sciences. These disciplinary perspectives are reviewed through the international literature and in a New Zealand context. A conceptual framework for research into the human dimensions of global environmental change is provided based on the knowledge-base developed from the both international and New Zealand research. Core concepts, such as found in multidisciplinary and transdisciplinary research are reviewed in historical context to form the basis for a specific research framework for New Zealand. Some general needs for research are derived from the New Zealand framework. The remainder of this report details the methods used for drawing out expert opinion on the priorities for research on the human dimensions of global environmental change in the context of information, knowledge and method. Thirty-eight interviews and two hui were conducted and 254 research needs defined by questions were obtained. A modified Delphi technique was applied to the questions and prioritised lists of responses are provided. The final list
Provides European/Pakeha and Māori perspectives. Divergence and convergence between the European/Pakeha and Māori responses is provided, as well as justification for the prioritisation stance that is taken. This research had four outputs: A conceptual framework for considering the human dimensions of global environmental changes and research contributions; A description of the gaps in knowledge that impede effective response to global change and provisional research needs: one for New Zealand generally, and one on Māori in particular; A network of end-users and research providers that are cognisant of, and contributors to, the human dimensions research assessment; A set of priority research needs pertaining to the human dimensions of global environmental change, in a form suitable for use by FRST.

year: 2005-12-01