Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.
Similarly to , active learning is an interactive framework for deciding what data points to collect in order to train a classifier or a regression model. Unlike active search, (i) its main objective is to improve the generalization performance of a model with as few label queries as possible, and (ii) the set of unlabeled points does not grow based on the collected points. A slew of active learning techniques have been proposed for non-relational data settings, including some tailored for logistic regression @cite_30 , for dealing with streamed data @cite_8 and for the case of extreme class imbalance @cite_19 . Although the retrieval of target nodes can benefit from an accurate model, it is unlikely that active learning heuristics (e.g., uncertainty sampling @cite_21 ) for training a single classifier can be used for without sacrificing performance. However, it may be possible to adapt active learning techniques proposed for training classifier ensembles (e.g., query by committee @cite_4 ) in such a way that, at the same time we collect points on which many classifiers disagree, we ensure that promising candidates among border nodes are queried before the sampling budget is exhausted.
@cite_30 @cite_4 @cite_8 @cite_21 @cite_19
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Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural first step in providing robust solutions for active learning across a wide variety of exponential models including maximum entropy, generalized linear, log-linear, and conditional random field models. For the logistic regression model we re-derive the variance reduction method known in experimental design circles as A-optimality.' We then run comparisons against different variations of the most widely used heuristic schemes: query by committee and uncertainty sampling, to discover which methods work best for different classes of problems and why. We find that among the strategies tested, the experimental design methods are most likely to match or beat a random sample baseline. The heuristic alternatives produced mixed results, with an uncertainty sampling variant called margin sampling and a derivative method called QBB-MM providing the most promising performance at very low computational cost. Computational running times of the experimental design methods were a bottleneck to the evaluations. Meanwhile, evaluation of the heuristic methods lead to an accumulation of negative results. We explore alternative evaluation design parameters to test whether these negative results are merely an artifact of settings where experimental design methods can be applied. The results demonstrate a need for improved active learning methods that will provide reliable performance at a reasonable computational cost. We propose an algorithm called query by commitee , in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement . The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms. We present a generalized framework for active inference, the selective acquisition of labels for cases at prediction time in lieu of using the estimated labels of a predictive model. We develop techniques within this framework for classifying in an online setting, for example, for classifying the stream of web pages where online advertisements are being served. Stream applications present novel complications because (i) at the time of label acquisition, we don't know the set of instances that we will eventually see, (ii) instances repeat based on some unknown (and possibly skewed) distribution. We combine ideas from decision theory, cost-sensitive learning, and online density estimation. We also introduce a method for on-line estimation of the utility distribution, which allows us to manage the budget over the stream. The resulting model tells which instances to label so that by the end of each budget period, the budget is best spent (in expectation). The main results show that: (1) our proposed approach to active inference on streams can indeed reduce error costs substantially over alternative approaches, (2) more sophisticated online estimations achieve larger reductions in error. We next discuss simultaneously conducting active inference and active learning. We show that our expected-utility active inference strategy also selects good examples for learning. We close by pointing out that our utility-distribution estimation strategy can also be applied to convert pool-based active learning techniques into budget-sensitive online active learning techniques. Extreme class skew is a hurdle in many machine learning tasks. In such skewed settings, traditional methods for procuring labeled examples, including random sampling and active learning, are often ineective| they struggle to nd representative minority examples. The framework of Dual Supervision, which incorporates feature-based background information into traditional supervised learning, provides one avenue to combat this problem. However, active learning for feature information (feature labeling), like active learning, is often not resilient to extreme class skew. In this work, we present an alternative to active feature labeling, Guided Feature Labeling. In this paradigm, human domain experts are tasked with