A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

MBE

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 105-109).Software developers use modeling to explore design alternatives before investing in the higher costs of building the full system. Unlike constructing specific examples, constructing general models is challenging and error-prone. Modeling By Example (MBE) is a new tool designed to help programmers construct general models faster and without errors. Given an object model and an acceptable, or included, example, MBE generates near-hit and near-miss examples for the user to mark as included or not by their mental goal model. The marked examples form a training data-set from which MBE constructs the user's general model. By generating examples dynamically to direct its own learning, MBE learns the concrete goal model with a significantly smaller training data set size than conventional instance-based learning techniques. Empirical experiments show that MBE is a practical solution for constructing simple structural models, but even with a number of optimizations to improve performance does not scale to learning complex models.by Lucy Mendel.M.Eng

Learning Prototypical Concept Descriptions

We describe a new representation for learning concepts that differs from the traditional decision tree and rule approach. This representation, called prototypical concept descriptions, can represent several prototypes for a concept. We also describe PL, our algorithm for learning these prototypes, and demonstrate that prototypical concept descriptions can, in some situations, classify more accurately than standard Machine Learning algorithms. More importantly, we show that they yield more stable descriptions when applied in noisy and dynamic situations. 1 INTRODUCTION Many machine learning algorithms use the minimum description length (MDL) (Rissanen 1976) bias to choose between two or more theories that classify equally well. Examples include most decision tree methods such as ID3 (Quinlan 1986) and other rule based algorithms, such as CN2 (Clark & Boswell 1991). By choosing only those features that discriminate classes as part of the learned theory and preferring simple..