16773487
A bias/variance decomposition for models using collective inference
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the learning process, but in relational domains, the inference process used for prediction introduces an additional source of error. Collective inference techniques introduce additional error, both through the use of approximate inference algorithms and through variation in the availability of test-set information. To date, the impact of inference error on model performance has not been investigated. We propose a new bias/variance framework that decomposes loss into errors due to both the learning and inference processes. We evaluate the performance of three relational models on both synthetic and real-world datasets and show that (1) inference can be a significant source of error, and (2) the models exhibit different types of errors as data characteristics are varied.
5327848
Adjusting for Multiple Comparisons in Decision Tree Pruning
Pruning is a common technique to avoid overfitting in decision trees. Most pruning techniques do not account for one important factor — multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. Making multiple comparisons produces incorrect inferences about model accuracy. We examine a method that adjusts for multiple comparisons when pruning decision trees - Bonferroni pruning. In experiments with artificial and realistic datasets, Bonferroni pruning produces smaller trees that are at least as accurate as trees pruned using other common approaches.
23300308
Social networks: looking ahead
By now, online social networks have become an indispensable part of both online and offline lives of human beings. A large fraction of time spent online by a user is directly influence by the social networks to which he/she belongs. This calls for a deeper examination of social networks as large-scale dynamic objects that foster efficient person-person interaction.
 The goal of our panel is to discuss social networks from various research angles. In particular, we plan to focus on the following broad research-related topics: large scale data mining, algorithmic questions, sociological aspects, privacy, web search, etc. We will also discuss the business and societal impacts of social networks. Each of these topics has generated a lot of research in recent years and while taking stock of what has been done, we will also be discussing the directions in which these topics are headed, from both science and society points of view. Our panel will consist of eminent researchers, who have worked/been working on an eclectic and diverse mix of problems in social networks
9934296
MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks
Disruption-tolerant networks (DTNs) attempt to route network messages via intermittently connected nodes. Routing in such environments is difficult because peers have little information about the state of the partitioned network and transfer opportunities between peers are of limited duration. In this paper, we propose MaxProp, a protocol for effective routing of DTN messages. MaxProp is based on prioritizing both the schedule of packets transmitted to other peers and the schedule of packets to be dropped. These priorities are based on the path likelihoods to peers according to historical data and also on several complementary mechanisms, including acknowledgments, a head-start for new packets, and lists of previous intermediaries. Our evaluations show that MaxProp performs better than protocols that have access to an oracle that knows the schedule of meetings between peers. Our evaluations are based on 60 days of traces from a real DTN network we have deployed on 30 buses. Our network, called UMassDieselNet, serves a large geographic area between five colleges. We also evaluate MaxProp on simulated topologies and show it performs well in a wide variety of DTN environments.
947628
Leveraging relational autocorrelation with latent group models
The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. Recent research has explored the use of collective inference techniques to exploit this phenomenon. These techniques achieve significant performance gains by modeling observed correlations among class labels of related instances, but the models fail to capture a frequent cause of autocorrelation - the presence of underlying groups that influence the attributes on a set of entities. We propose a latent group model (LGM) for relational data, which discovers and exploits the hidden structures responsible for the observed autocorrelation among class labels. Modeling the latent group structure improves model performance, increases inference efficiency, and enhances our understanding of the datasets. We evaluate performance on three relational classification tasks and show that LGM outperforms models that ignore latent group structure, particularly when there is little information with which to seed inference.
4518008
Exploiting relational structure to understand publication patterns in high-energy physics
We analyze publication patterns in theoretical high-energy physics using a relational learning approach. We focus on four related areas: understanding and identifying patterns of citations, examining publication patterns at the author level, predicting whether a paper will be accepted by specific journals, and identifying research communities from the citation patterns and paper text. Each of these analyses contributes to an overall understanding of theoretical high-energy physics.
69276
Fast and efficient voltage scheduling by evolutionary slack distribution
To minimize energy consumption by voltage scaling in design of heterogeneous real-time embedded systems, it is necessary to perform two distinct tasks: task scheduling (TS) and voltage selection (VS). Techniques proposed to date either are fast but yield inefficient results, or output efficient solutions after many slow iterations. As a core problem to solve in the inner loop of a system-level optimization cycle, it is critical that the algorithm be fast while producing high quality results. This paper presents a new technique called Evolutionary Relative Slack Distribution Voltage Scheduling (ERSD-VS) that achieves both speed and efficiency. It addresses priority adjustment and slack distribution issues with low cost heuristics. Experimental results from running publicly available testbenches show up to 42% energy saving compared to a published technique called EVEN-VS. It also shows up to 70 times speed improvement compared to an efficient technique called EE-GLSA.
38784454
Plant location with minimum inventory
We present an integer programming model for plant location with inventory costs. The linear programming relaxation has been solved by Dantzig-Wolfe decomposition. In this case the subproblems reduce to the minimum cut problem. We have used subgradient optimization to accelerate the convergence of the D-W algorithm. We present our experience with problems arising in the design of a distribution network for computer spare parts. In most cases, from a fractional solution we were able to derive integer solutions within 4% of optimality.
15010205
Introduction to the special issue on multi-relational data mining and statistical relational learning
Within the field of machine learning, it has long been recognized that the difficulty of learning is related, among other things, to the representational complexity of the knowledge that is input to the learner. Many machine learning approaches learn from examples that have a relatively simple, “standard”, representation, and that are drawn independently from an identical distribution (“i.i.d.”). In many application domains for machine learning, however, individuals may have a richer internal structure than the standard representation allows us to represent, and there may be relationships between them that invalidate the i.i.d.-assumption. As a result, standard learning techniques may not work very well in these domains. More specifically, the majority of learning approaches have considered cases where each individual data element is described using a fixed set of attributes, each of which has a simple domain that contains atomic (non-decomposable) values, which may be continuous or discrete. But data, or knowledge in general, is not always available in this restricted format, nor can it always easily be converted into it. Individual data elements might be described in a way that is inherently more complex, for instance, as sets or graphs. Such structures can be summarized by listing a number of properties (the size of a set, number of nodes or edges in a graph, or in general certain distributional characteristic of these sets or graphs), but such a summary is always an abstraction of the actual information and as such gives rise to loss of information. Note that this complex description need not describe the “internal structure” of an example. There are many cases where individuals have a simple internal structure, but are related to each other or to other external objects. If we want to take these relationships into account when learning, the description of such an individual includes its local environment and therefore becomes relational. Ignoring this relational structure not only causes potentially useful information to be ignored; it may actually be harmful to the learner. For instance, it has been shown that the violation of the i.i.d.-assumption causes the standard heuristics used by learners to be misguided.