k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

k-Nearest Neighbour Classifiers - A Tutorial

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data.This paper is the second edition of a paper previously published as a technical report . Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods

Underestimation Bias and Underfitting in Machine Learning

. Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias

Taking a book off the shelf in a virtual library

We present the results of a small-scale study in which participants interacted with a physical book. Their book selection and book opening gestures provide design insights for the interface to a virtual reality library

An investigation of Early Childhood Staff and Their Transition to the New Western Australian Humanities and Social Sciences Curriculum

In 2017, a new Humanities and Social Sciences (HASS) curriculum was introduced into all of Western Australia’s classrooms. The aim of this study was to investigate how teachers transitioned to that new HASS curriculum. Using case study methodology, the experiences, opportunities and challenges faced by the early childhood (EC) staff in two Perth schools were investigated as they prepared for and implemented a new HASS Curriculum. The results suggested the need for strong leadership in times of change. The results also indicated that these small, independent schools needed good resources and professional development to help understand the changes. The research is significant because it starts a much-needed conversation about prioritising HASS in the early years of schooling as well as addressing the challenges faced by early childhood teachers as they transition to teaching new curriculum in a core learning area

A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering

The problem of concept drift has recently received con- siderable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on con- cept drift shows that among the most promising ap- proaches are ensembles and a variety of techniques for ensemble construction has been proposed. In this pa- per we compare the ensemble approach to an alternative lazy learning approach to concept drift whereby a sin- gle case-based classifier for spam filtering keeps itself up-to-date through a case-base maintenance protocol. We present an evaluation that shows that the case-base maintenance approach is more effective than a selection of ensemble techniques. The evaluation is complicated by the overriding importance of False Positives (FPs) in spam filtering. The ensemble approaches can have very good performance on FPs because it is possible to bias an ensemble more strongly away from FPs than it is to bias the single classifer. However this comes at consid- erable cost to the overall accurac

Assessing communicative participation in preschool children with the Focus on the Outcomes of Communication Under Six: a scoping review

Aim: To describe uses of the Focus on the Outcomes of Communication Under Six (FOCUS) in research since its publication in 2010. Method: Six databases were searched for the term ‘Focus on the Outcomes of Communication Under Six’. With additional searches we ascertained 70 articles, of which 25 met inclusion criteria for full review and data extraction. Results: The FOCUS has been used in research across multiple countries, purposes, populations, contexts, and versions. Evaluative studies have described the development of children’s communicative participation skills and factors that impact development of communicative participation; the impact of specific interventions on communicative participation; how FOCUS captures change relative to measures of impairment; and how FOCUS performs when used at different intervals. Adaptations included use of the FOCUS as a descriptive or discriminative tool; use with children outside the validated age range; using select items; and use with typically developing children. Interpretation: FOCUS is used worldwide in research and practice, and much has been learned about children’s communicative participation. Future research is needed to explore the relationship between children’s impairments and their communicative participation, develop a FOCUS App, and develop and validate of a school-age FOCUS