Automatically classifying test results by semi-supervised learning

A key component of software testing is deciding whether a test case has passed or failed: an expensive and error-prone manual activity. We present an approach to automatically classify passing and failing executions using semi-supervised learning on dynamic execution data (test inputs/outputs and execution traces). A small proportion of the test data is labelled as passing or failing and used in conjunction with the unlabelled data to build a classifier which labels the remaining outputs (classify them as passing or failing tests). A range of learning algorithms are investigated using several faulty versions of three systems along with varying types of data (inputs/outputs alone, or in combination with execution traces) and different labelling strategies (both failing and passing tests, and passing tests alone). The results show that in many cases labelling just a small proportion of the test cases – as low as 10% – is sufficient to build a classifier that is able to correctly categorise the large majority of the remaining test cases. This has important practical potential: when checking the test results from a system a developer need only examine a small proportion of these and use this information to train a learning algorithm to automatically classify the remainder

New Paradigms for Active Learning

In traditional active learning, learning algorithms (or learners) mainly focus on the performance of the final model built and the total number of queries needed for learning a good model. However, in many real-world applications, active learners have to focus on the learning process for achieving finer goals, such as minimizing the number of mistakes in predicting unlabeled examples. These learning goals are common and important in real-world applications. For example, in direct marketing, a sales agent (learner) has to focus on the process of selecting customers to approach, and tries to make correct predictions (i.e., fewer mistakes) on the customers who will buy the product. However, traditional active learning algorithms cannot achieve the finer learning goals due to the different focuses. In this thesis, we study how to control the learning process in active learning such that those goals can be accomplished. According to various learning tasks and goals, we address four new active paradigms as follows. The first paradigm is learning actively and conservatively. Under this paradigm, the learner actively selects and predicts the most certain example (thus, conservatively) iteratively during the learning process. The goal of this paradigm is to minimize the number of mistakes in predicting unlabeled examples during active learning. Intuitively the conservative strategy is less likely to make mistakes, i.e., more likely to achieve the learning goal. We apply this new learning strategy in an educational software, as well as direct marketing. The second paradigm is learning actively and aggressively. Under this paradigm, unlabeled examples and multiple oracles are available. The learner actively selects the best multiple oracles to label the most uncertain example (thus, aggressively) iteratively during the learning process. The learning goal is to learn a good model with guaranteed label quality. The third paradigm is learning actively with conservative-aggressive tradeoff. Under this learning paradigm, firstly, unlabeled examples are available and learners are allowed to select examples actively to learn. Secondly, to obtain the labels, two actions can be considered: querying oracles and making predictions. Lastly, cost has to be paid for querying oracles or for making wrong predictions. The tradeoff between the two actions is necessary for achieving the learning goal: minimizing the total cost for obtaining the labels. The last paradigm is learning actively with minimal/maximal effort. Under this paradigm, the labels of the examples are all provided and learners are allowed to select examples actively to learn. The learning goal is to control the learning process by selecting examples actively such that the learning can be accomplished with minimal effort or a good model can be built fast with maximal effort. For each of the four learning paradigms, we propose effective learning algorithms accordingly and demonstrate empirically that related learning problems in real applications can be solved well and the learning goals can be accomplished. In summary, this thesis focuses on controlling the learning process to achieve fine goals in active learning. According to various real application tasks, we propose four novel learning paradigms, and for each paradigm we propose efficient learning algorithms to solve the learning problems. The experimental results show that our learning algorithms outperform other state-of-the-art learning algorithms

SALIC: Social Active Learning for Image Classification

In this paper, we present SALIC, an active learning method for selecting the most appropriate user tagged images to expand the training set of a binary classifier. The process of active learning can be fully automated in this social context by replacing the human oracle with the images' tags. However, their noisy nature adds further complexity to the sample selection process since, apart from the images' informativeness (i.e., how much they are expected to inform the classifier if we knew their label), our confidence about their actual label should also be maximized (i.e., how certain the oracle is on the images' true contents). The main contribution of this work is in proposing a probabilistic approach for jointly maximizing the two aforementioned quantities. In the examined noisy context, the oracle's confidence is necessary to provide a contextual-based indication of the images' true contents, while the samples' informativeness is required to reduce the computational complexity and minimize the mistakes of the unreliable oracle. To prove this, first, we show that SALIC allows us to select training data as effectively as typical active learning, without the cost of manual annotation. Finally, we argue that the speed-up achieved when learning actively in this social context (where labels can be obtained without the cost of human annotation) is necessary to cope with the continuously growing requirements of large-scale applications. In this respect, we demonstrate that SALIC requires ten times less training data in order to reach the same performance as a straightforward informativeness-agnostic learning approach

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd