Prediction of protein-protein interactions using one-class classification methods and integrating diverse data

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

Cost-Quality Trade-Offs in One-Class Active Learning

Active learning is a paradigm to involve users in a machine learning process. The core idea of active learning is to ask a user to annotate a specific observation to improve the classification performance. One important application of active learning is detecting outliers, i.e., unusual observations that deviate from the regular ones in a data set. Applying active learning for outlier detection in practice requires to design a system that consists of several components: the data, the classifier that discerns between inliers and outliers, the query strategy that selects the observations for feedback collection, and an oracle, e.g., the human expert that annotates the queries. Each of these components and their interplay influences the classification quality. Naturally, there are cost budgets limiting certain parts of the system, e.g., the number of queries one can ask a human. Thus, to configure efficient active learning systems, one must decide on several trade-offs between costs and quality. The existing literature on active learning systems does not provide an overview nor a formal description of the cost-quality trade-offs of active learning. All this makes the configuration of efficient active learning systems in practice difficult. In this thesis, we study different cost-quality trade-offs that are pivotal for configuring an active learning system for outlier detection. We first provide an overview of the costs of an active learning system. Then, we analyze three important trade-offs and propose ways to model and quantify them. In our first contribution, we study how one can reduce classification training costs by training only on a sample of the data set. We formalize the sampling trade-off between classifier training costs and resulting quality as an optimization problem and propose an efficient algorithm to solve it. Compared to the existing sampling methods in literature, our approach guarantees that a classifier trained on our sample makes the same predictions as if trained on the complete data set. We can therefore reduce the classification training costs without a loss of classification quality. In our second contribution, we investigate how selecting multiple queries allows trading off costs against quality. So-called batch queries reduce classifier training costs because the system only updates the classifier once for each batch. But the annotation of a batch may give redundant information, which reduces the achievable quality with a fixed query budget. We are the first to consider batch queries for outlier detection, a generalization of the more common case to query sequentially. We formalize batch active learning and propose several strategies to construct batches by modeling the expected utility of a batch. In our third contribution, we propose query synthesis for outlier detection. Query synthesis allows to artificially generate queries at any point in the data space without being restricted by a pool of query candidates. We propose a framework to efficiently synthesize queries and develop a novel query strategy to improve the generalization of a classifier beyond a biased data set with active learning. For all contributions, we derive recommendations for the cost-quality trade-offs from formal investigations and empirical studies to facilitate the configuration of robust and efficient active learning systems for outlier detection

Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively