A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Assessing the effects of power quality on partial discharge behaviour through machine learning

Partial discharge (PD) is commonly used as an indicator of insulation health in high voltage equipment, but research has indicated that power quality, particularly harmonics, can strongly influence the discharge behaviour and the corresponding pattern observed. Unacknowledged variation in harmonics of the excitation voltage waveform can influence the insulation's degradation, leading to possible misinterpretation of diagnostic data and erroneous estimates of the insulation's ageing state, thus resulting in inappropriate asset management decisions. This paper reports on a suite of classifiers for identifying pertinent harmonic attributes from PD data, and presents results of techniques for improving their accuracy. Aspects of PD field monitoring are used to design a practical system for on-line monitoring of voltage harmonics. This system yields a report on the harmonics experienced during the monitoring period

Interpretation of partial discharge activity in the presence of harmonics

Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification of harmonic influenced partial discharge patterns. The Total Harmonic Distortion (THD) index was varied to a maximum of 40%. The results show progressive development for interpretation of condition monitoring data, improving the asset manager's holistic view of an asset's health

Anomaly Detection Based on Aggregation of Indicators

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Identifying harmonic attributes from online partial discharge data

Partial discharge (PD) monitoring is a key method of tracking fault progression and degradation of insulation systems. Recent research discovered that the harmonic regime experienced by the plant also affects the PD pattern, questioning the conclusions about equipment health drawn from PD data. This paper presents the design and creation of an online system for harmonic circumstance monitoring of distribution cables, using only PD data. Based on machine learning techniques, the system can assess the prevalence of the 5th and 7th harmonic orders over the monitoring period. This information is key for asset managers to draw correct conclusions about the remaining life of polymeric cable insulation, and prevent overestimation of the degradation trend

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.Comment: 10 pages, KDD 201