Telstra

About: Telstra is a company organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: The Internet & Support vector machine. The organization has 307 authors who have published 325 publications receiving 5886 citations. The organization is also known as: Telstra Corporation Limited & Telstra Corporation.

Extreme re-balancing for SVMs: a case study

Abstract: There are many practical applications where learning from single class examples is either, the only possible solution, or has a distinct performance advantage. The first case occurs when obtaining examples of a second class is difficult, e.g., classifying sites of "interest" based on web accesses. The second situation is exemplified by the gene knock-out experiments for understanding Aryl Hydrocarbon Receptor signalling pathway that provided the data for the second task of the KDD 2002 Cup, where minority one-class SVMs significantly outperform models learnt using examples from both classes.This paper explores the limits of supervised learning of a two class discrimination from data with heavily unbalanced class proportions. We focus on the case of supervised learning with support vector machines. We consider the impact of both sampling and weighting imbalance compensation techniques and then extend the balancing to extreme situations when one of the classes is ignored completely and the learning is accomplished using examples from a single class.Our investigation with the data for KDD 2002 Cup as well as text benchmarks such as Reuters Newswire shows that there is a consistent pattern of performance differences between one and two-class learning for all SVMs investigated, and these patterns persist even with aggressive dimensionality reduction through automated feature selection. Using insight gained from the above analysis, we generate synthetic data showing similar pattern of performance.

Optimising area under the ROC curve using gradient descent

Abstract: This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n2) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n.) This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.

A document categorisation system

Abstract: A document categorisation system, including a clusterer for generating clusters of related electronic documents based on features extracted from said documents, and a filter module for generating a filter on the basis of said clusters to categorise further documents received by said system. The system may include an editor for manually browsing and modifying the clusters. The categorisation of the documents is based on n-grams, which are used to determine significant features of the documents. The system includes a trend analyzer for determining trends of changing document categories over time, and for identifying novel clusters. The system may be implemented as a plug-in module for a spreadsheet application, providing a convenient means for one-off or ongoing analysis of text entries in a worksheet.

When customers disappoint: A model of relational internal marketing and customer complaints

Abstract: The objective of this study is to examine internal marketing relationships and their influence on salesperson attitudes and behaviors in retail store environments. The authors investigate the moderating role of customer complaining behavior on the nature of these relationships. Specifically, they examine the relationship between organization-employee and supervisor-employee relationships and their association with salesperson job motivation and commitment to customer service. Customer complaints are expected to have differential moderating effects on the relationship between organizational and supervisory support and these salesperson outcomes. Our hypotheses were tested using a sample of 392 retail employees within 115 stores of a national retail organization. The model was partially supported. Theoretical and managerial implications are explored.

Including landmarks in routing instructions

Abstract: This article addresses the problem of incorporating cognitively salient landmarks in computer-generated navigation instructions. On the basis of a review of the existing literature in the domain of navigation with landmarks, the article develops algorithms for generating routing instructions that include references to landmarks. The most basic algorithm uses a new weighting model to annotate simple routes with references to landmarks. A key novel feature of this algorithm is that it depends only on commonly available data and generic capabilities of existing web mapping environments. A suite of extensions are also proposed for improving the cognitive ergonomics of the basic landmark instructions. A case study, implemented within a national online routing system, demonstrates practicality of the approach. The article then concludes by reviewing a range of further issues for future work.