12992442
Relational data pre-processing techniques for improved securities fraud detection
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarely structured appropriately for knowledge discovery, and they often contain variables whose meanings change across different subsets of the data. We describe how these challenges were addressed in a collaborative analysis project undertaken by the University of Massachusetts Amherst and the National Association of Securities Dealers(NASD). We describe several methods for data pre-processing that we applied to transform a large, dynamic, and relational dataset describing nearly the entirety of the U.S. securities industry, and we show how these methods made the dataset suitable for learning statistical relational models. To better utilize social structure, we first applied known consolidation and link formation techniques to associate individuals with branch office locations. In addition, we developed an innovative technique to infer professional associations by exploiting dynamic employment histories. Finally, we applied normalization techniques to create a suitable class label that adjusts for spatial, temporal, and other heterogeneity within the data. We show how these pre-processing techniques combine to provide the necessary foundation for learning high-performing statistical models of fraudulent activity.
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.
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.
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.
3495683
The case for anomalous link detection
In this paper, we describe the challenges inherent to the Link Prediction (LP) problem in multirelational data mining, and explore the reasons why many LP models have performed poorly. We present the alternate (and complimentary) task of Anomalous Link Discovery (ALD) and qualitatively demonstrate the effectiveness of simple LP models for the ALD task.
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.