The Transductive Confidence Machine Nearest Neighbours (TCMNN) algorithm and a supporting, simple user interface was developed. Different settings of the TCMNN algorithms' parameters were tested on medical data sets, in addition to the use of different Minkowski metrics and polynomial kernels. The effect of increasing the number of nearest neighbours and marking results with significance was also investigated. SVM implementation of the Transductive Confidence Machine was compared with Nearest Neighbours implementation. The application of neural networks was investigated as a useful comparison to the transductive algorithms.

Employing multiple workers to label data for machine learning models has become increasingly important in recent years with greater demand to collect huge volumes of labelled data to train complex models while mitigating the risk of incorrect and noisy labelling. Whether it is large scale data gathering on popular crowd-sourcing platforms or smaller sets of workers in high-expertise labelling exercises, there are various methods recommended to gather a consensus from employed workers and establish ground-truth labels. However, there is very little research on how the various parameters of a labelling task can impact said methods. These parameters include the number of workers, worker expertise, number of labels in a taxonomy and sample size. In this paper, Majority Vote, CrowdTruth and Binomial Expectation Maximisation are investigated against the permutations of these parameters in order to provide better understanding of the parameter settings to give an advantage in ground-truth inference. Findings show that both Expectation Maximisation and CrowdTruth are only likely to give an advantage over majority vote under certain parameter conditions, while there are many cases where the methods can be shown to have no major impact. Guidance is given as to what parameters methods work best under, while the experimental framework provides a way of testing other established methods and also testing new methods that can attempt to provide advantageous performance where the methods in this paper did not. A greater level of understanding regarding optimal crowd-sourcing parameters is also achieved.

First, let's set things up in R by loading the necessary package and importing the data into R. the class package will be used to run the k-nearest neighbors algorithm. We will also use a specific seed so that you can reproduce this in R yourself. Importing the data, let's take a look at the basic structure of the dataset. The first variable, id, is there simply as a unique identifier for each observation. We will take it out for the purposes of our analysis.