Abstract. Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.

Abstract. The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to measure the closeness between objects, two novel KNN procedures, Locally Informative-KNN (LI-KNN) and Globally Informative-KNN (GI-KNN), are proposed. By selecting a subset of most informative objects from neighborhoods, our methods exhibit stability to the change of input parameters, number of neighbors(K) and informative points (I). Experiments on UCI benchmark data and diverse real-world data sets indicate that our approaches are application-independent and can generally outperform several popular KNN extensions, as well as SVM and Boosting methods.

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The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.

We evaluate the link between chief executive officer (CEO) industry tournament incentives (ITIs) and the product-market benefits of corporate liquidity. We find that ITIs increase the level and marginal value of cash holdings. Furthermore, ITIs strengthen the relation between excess cash and market-share gains, especially for firms that face significant competitive threats. Additionally, for firms with excess cash, higher ITIs lead to increased research and development (R&D) expenses, capital expenditures, and spending on focused acquisitions as well as reduced payouts. Overall, our findings suggest that ITIs increase the value of cash by incentivizing CEOs to deploy cash strategically to capture its product-market benefits.