10748648
Avoiding Bias when Aggregating Relational Data with Degree Disparity
A common characteristic of relational data sets --degree disparity--can lead relational learning algorithms to discover misleading correlations. Degree disparity occurs when the frequency of a relation is correlated with the values of the target variable. In such cases, aggregation functions used by many relational learning algorithms will result in misleading correlations and added complexity in models. We examine this problem through a combination of simulations and experiments. We show how two novel hypothesis testing procedures can adjust for the effects of using aggregation functions in the presence of degree disparity.
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
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.
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.
39613394
Reports on the AAAI Fall Symposia (November 1999 and November 1998)
The 1999 Association for the Advancement of Artificial Intelligence Fall Symposium Series was held Friday through Sunday, 5-7 November 1999, at the Sea Crest Oceanfront Resort and Conference Center. The titles of the five symposia were (1) Modal and Temporal Logics-Based Planning for Open Networked Multimedia Systems; (2) Narrative Intelligence; (3) Psychological Models of Communication in Collaborative Systems; (4) Question-Answering Systems; and (5) Using Layout for the Generation, Understanding, or Retrieval of Documents.
1684706
Using relational knowledge discovery to prevent securities fraud
We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world's largest private-sector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD's limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but have not been easily used before now. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD.
543844
Supply chain simulation: distributed supply chain simulation using a generic job running framework
For supply chain performance simulation that involves aggregating results from multiple runs of the same underlying model, simulation iterations can be distributed to networked computing resources to achieve significant speedup. This paper presents a generic distributed job running framework that facilitates such high performance supply chain simulation. We first introduce a supply chain modeling and simulation tool developed by IBM Research, and summarize the strategy to enhance it. A closer look is then taken at a generic job running framework we designed and how it was used to bring the distributed simulation capability to the tool. After reviewing an ongoing effort to integrate the new tool with the IBM MathGrid environment, we conclude the paper with a brief discussion of our future work.
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.