waashk/dblp huggingface dataset:
voting with a parameterized veto strategy: solving the kdd cup 2006 problem by means of a classifier committee. this paper presents our winner solution for the kdd cup 2006 problem. it is based on the results of three different supervised learning techniques which are then combined in a classifier committee, and finally a single solution is obtained with a voting procedure. the voting procedure assigns weights to each member of the committee according to their average performance on a ten-fold cross-validation test and it also takes into account the confidence values returned by the three algorithms. the final decision of the committee is determined by means of a parameterized veto strategy, which takes into consideration the maximal allowed error rate beside the confidence values of the committee members. the solution presented here won task 2 and became runner-up at task 1 in the competition.
resolving citations in a paper repository. in this paper, we describe our process of creating a citation graph from a given repository of physics publications in latex format. the task involved a series of information extraction, data cleaning, matching and ranking steps. this paper describes the challenges we faced along the way and the issues involved in resolving them.
modifying boosted trees to improve performance on task 1 of the 2006 kdd challenge cup. task 1 of the 2006 kdd challenge cup required classification of pulmonary embolisms (pes) using variables derived from computed tomography angiography. we present our approach to the challenge and justification for our choices. we used boosted trees to perform the main classification task, but modified the algorithm to address idiosyncrasies of the scoring criteria. the two main modifications were: 1) changing the dependent variable in the training set to account for multiple pes per patient, and 2) incorporating neighborhood information through augmentation of the set of predictor variables. both of these resulted in measurable predictive improvement. in addition, we discuss a statistically based method for setting the classification threshold.
dynamic social network analysis using latent space models. this paper explores two aspects of social network modeling. first, we generalize a successful static model of relationships into a dynamic model that accounts for friendships drifting over time. second, we show how to make it tractable to learn such models from data, even as the number of entities n gets large. the generalized model associates each entity with a point in p-dimensional euclidean latent space. the points can move as time progresses but large moves in latent space are improbable. observed links between entities are more likely if the entities are close in latent space. we show how to make such a model tractable (sub-quadratic in the number of entities) by the use of appropriate kernel functions for similarity in latent space; the use of low dimensional kd-trees; a new efficient dynamic adaptation of multidimensional scaling for a first pass of approximate projection of entities into latent space; and an efficient conjugate gradient update rule for non-linear local optimization in which amortized time per entity during an update is o(log n). we use both synthetic and real-world data on up to 11,000 entities which indicate near-linear scaling in computation time and improved performance over four alternative approaches. we also illustrate the system operating on twelve years of nips co-authorship data.
feature selection for text categorization on imbalanced data. a number of feature selection metrics have been explored in text categorization, among which information gain (ig), chi-square (chi), correlation coefficient (cc) and odds ratios (or) are considered most effective. cc and or are one-sided metrics while ig and chi are two-sided. feature selection using one-sided metrics selects the features most indicative of membership only, while feature selection using two-sided metrics implicitly combines the features most indicative of membership (e.g. positive features) and non-membership (e.g. negative features) by ignoring the signs of features. the former never consider the negative features, which are quite valuable, while the latter cannot ensure the optimal combination of the two kinds of features especially on imbalanced data. in this work, we investigate the usefulness of explicit control of that combination within a proposed feature selection framework. using multinomial na&iuml;ve bayes and regularized logistic regression as classifiers, our experiments show both great potential and actual merits of explicitly combining positive and negative features in a nearly optimal fashion according to the imbalanced data.
profiling your customers using bayesian networks. this report describes a complete knowledge discovery session using bayesware discoverer, a program for the induction of bayesian networks from incomplete data. we build two causal models to help an american charitable organization understand the characteristics of respondents to direct mail fund raising campaigns. the first model is a bayesian network induced from the database of 96,376 lapsed donors to the june '97 renewal mailing. the network describes the dependency of the probability of response to the renewal mail on a subset of the variables in the database. the second model is a bayesian network representing the dependency of the dollar amount of the gift on the variables in the same reduced database. this model is induced from the 5% of cases in the database corresponding to the respondents to the renewal campaign. the two models are used for both predicting the expected gift of a donor and understanding the characteristics of donors. these two uses can help the charitable organization to maximize the profit.
text mining for product attribute extraction. we describe our work on extracting attribute and value pairs from textual product descriptions. the goal is to augment databases of products by representing each product as a set of attribute-value pairs. such a representation is beneficial for tasks where treating the product as a set of attribute-value pairs is more useful than as an atomic entity. examples of such applications include demand forecasting, assortment optimization, product recommendations, and assortment comparison across retailers and manufacturers. we deal with both implicit and explicit attributes and formulate both kinds of extractions as classification problems. using single-view and multi-view semi-supervised learning algorithms, we are able to exploit large amounts of unlabeled data present in this domain while reducing the need for initial labeled data that is expensive to obtain. we present promising results on apparel and sporting goods products and show that our system can accurately extract attribute-value pairs from product descriptions. we describe a variety of application that are built on top of the results obtained by the attribute extraction system.
link mining applications: progress and challenges. this article reviews a decade of progress in the area of link mining, focusing on application requirements and how they have and have not yet been addressed, especially in the area of complex event detection. it discusses some ongoing challenges and suggests ideas that could be opportunities for solutions. the most important conclusion of this article is that while there are many link mining techniques that work well for individual link mining tasks, there is not yet a comprehensive framework that can support a combination of link mining tasks as needed for many real applications.
data mining for improved cardiac care. cardiovascular disease (cvd) is the single largest killer in the world. although, several cvd treatment guidelines have been developed to improve quality of care and reduce healthcare costs, for a number of reasons, adherence to these guidelines remains poor. further, due to the extremely poor quality of data in medical patient records, most of today's healthcare it systems cannot provide significant support to improve the quality of cvd care (particularly in chronic cvd situations which contribute to the majority of costs).we present remind, a probabilistic framework for reliable extraction and meaningful inference from nonstructured data. remind integrates the structured and unstructured clinical data in patient records to automatically create high-quality structured clinical data. there are two principal factors that enable remind to overcome the barriers associated with inference from medical records. first, patient data is highly redundant -- exploiting this redundancy allows us to deal with the inherent errors in the data. second, remind performs inference based on external medical domain knowledge to combine data from multiple sources and to enforce consistency between different medical conclusions drawn from the data -- via a probabilistic reasoning framework that overcomes the incomplete, inconsistent, and incorrect nature of data in medical patient records.this high-quality structuring allows existing patient records to be mined to support guideline compliance and to improve patient care. however, once remind is configured for an institution's data repository, many other important clinical applications are also enabled, including: quality assurance; therapy selection for individual patients; automated patient identification for clinical trials; data extraction for research studies; and to relate financial and clinical factors. remind provides value across the continuum of healthcare, ranging from small physician practice databases to the most complex hospital it systems, from acute cardiac care to chronic cvd management, and to experimental research studies. remind is currently deployed across multiple disease areas over a total of 5,000,000 patients across the us.
qc@ust: our winning solution to query classification in kddcup 2005. in this paper, we describe our ensemble-search based approach, q2c@ust (http://webprojectl.cs.ust.hk/q2c/), for the query classification task for the kddcup 2005. there are two aspects to the key difficulties of this problem: one is that the meaning of the queries and the semantics of the predefined categories are hard to determine. the other is that there are no training data for this classification problem. we apply a two-phase framework to tackle the above difficulties. phase i corresponds to the training phase of machine learning research and phase ii corresponds to testing phase. in phase i, two kinds of classifiers are developed as the base classifiers. one is synonym-based and the other is statistics based. phase ii consists of two stages. in the first stage, the queries are enriched such that for each query, its related web pages together with their category information are collected through the use of search engines. in the second stage, the enriched queries are classified through the base classifiers trained in phase i. based on the classification results obtained by the base classifiers, two ensemble classifiers based on two different strategies are proposed. the experimental results on the validation dataset help confirm our conjectures on the performance of the q2c@ust system. in addition, the evaluation results given by the kddcup 2005 organizer confirm the effectiveness of our proposed approaches. the best f1 value of our two solutions is 9.6% higher than the best of all other participants' solutions. the average f1 value of our two submitted solutions is 94.4% higher than the average f1 value from all other submitted solutions.
introduction to the special issue on successful real-world data mining applications. since its inception, the field of data mining and knowledge discovery from databases has been driven by the need to solve practical problems [4]. from scaling to large databases and handling noisy and high-dimensional data to finding associational patterns in grocery store transaction data, data mining is a research area rich in application [1]. despite its practical roots few case studies of data mining applications have been published. the industrial track of the annual sigkdd conference has provided one such forum, but rarely do these papers present complete descriptions of deployed systems [2]. this special issue attempts to address the gap by showcasing the choices, strategies, and lessons learned from building a real-world data mining application. in a sense this collection is a follow-up to the first workshop on data mining case studies held during icdm-2006 [3]. this issue however introduces several new papers. of the 29 papers reviewed 10 papers were accepted. the papers come from a broad range of application areas including customer relationship management, medicine, taxation, and software development.
spatial data mining to support pandemic preparedness. effective detection of and response to pandemic disease outbreaks require significant advances in data mining. contributions to the recently held siam dm 2006 workshop on spatial data mining highlighted key challenges, directions, and progress in this context. we summarize here the main themes presented at the workshop as well as promising research directions for the data mining community.
vdm@ecml/pkdd2001: the international workshop on visual data mining at ecml/pkdd 2001. this brief report presents an overview of the international workshop on visual data mining, conducted on 4 september 2001 in conjunction with the 12th european conference on machine learning (ecml'01) and 5th european conference on principles and practice of knowledge discovery in databases (pkdd'01). it includes summary of the presentations and discussions, and provides pointers to relevant resources in the area.
network reconstruction from dynamic data. over the past decade, many powerful data mining techniques have been developed to analyze temporal and sequential data. the time is now fertile for addressing problems of larger scope under the purview of temporal data mining. the fourth sigkdd workshop on temporal data mining focused on the question: what can we infer about the structure of a complex dynamical system from observed temporal data? the goals of the workshop were to critically evaluate the need in this area by bringing together leading researchers from industry and academia, and to identify promising technologies and methodologies for doing the same. we provide a brief summary of the workshop proceedings and ideas arising out of the discussions.
mining source code elements for comprehending object-oriented systems and evaluating their maintainability. data mining and its capacity to deal with large volumes of data and to uncover hidden patterns has been proposed as a means to support industrial scale software maintenance and comprehension. this paper presents a methodology for knowledge acquisition from source code in order to comprehend an object-oriented system and evaluate its maintainability. we employ clustering in order to support semi-automated software maintenance and comprehension.a model and an associated process are provided, in order to extract elements from source code; k-means clustering is then applied on these data, in order to produce system overviews and deductions. the methodology is evaluated on jboss, a very large open source application server; results are discussed and conclusions are presented together with directions for future work.
championing of an ltv model at ltc. in this paper we report on the successful implementation of a lifetime value (ltv) forecasting system at a large telecommunications company (ltc). while some research results have been reported elsewhere on the technical challenges of modeling customer value, our experience suggests that a data mining system implementation can expect to encounter several organizational challenges that can impinge on its success. we provide a background on the application, and then analyze several success factors.
supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n). new genomic and proteomic technologies provide measurements of thousands of features for each case. this provides a context for enhanced discovery and false discovery. most statistical and machine learning procedures were not developed for the p>>n setting and the literature of dna microarray studies contains many examples of mis-use of analytic and computatinal methods such a cross-validation. this paper highlights some of key aspects of p>>n problems for identifying informative features and developing accurate classifiers.
utility-based anonymization for privacy preservation with less information loss. privacy becomes a more and more serious concern in applications involving microdata. recently, efficient anonymization has attracted much research work. most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. however, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. moreover, anonymized data is often used for analysis. as well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. the utility of attributes has not been considered in the previous methods. in this paper, we study the problem of utility-based anonymization. first, we propose a simple framework to specify utility of attributes. the framework covers both numeric and categorical data. second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
client-side web mining for community formation in peer-to-peer environments. in this paper we present a framework for forming interests-based peer-to-peer communities using client-side web browsing history. at the heart of this framework is the use of an order statistics-based approach to build communities with hierarchical structure. we have also carefully considered privacy concerns of the peers and adopted cryptographic protocols to measure similarity between them without disclosing their personal profiles. we evaluated our framework on a distributed data mining platform we have developed. the experimental results show that our framework could effectively build interests-based communities.
learning important models for web page blocks based on layout and content analysis. previous work shows that a web page can be partitioned into multiple segments or blocks, and often the importance of those blocks in a page is not equivalent. it has also been proven that differentiating noisy and unimportant blocks from pages can facilitate web mining, search and accessibility. however, no uniform approach and model has been presented to measure the importance of different blocks in a web page. through a user study, we found that people do have a consistent view about the importance of blocks in a web page. thus, we investigate how to find a model to automatically assign importance values to blocks in a web page. we formulate the block importance estimation as a learning problem. first, we use a vision-based page segmentation technique to partition a web page into semantic blocks with a hierarchical structure. then spatial features (such as position and size) and content features (such as the number of images and links) are extracted to construct a feature vector for each block. then, learning algorithms are used to train a model to assign importance to each block in the web page. in our experiments, the best model can achieve the performance with micro-f1 80.2% and micro-accuracy 86.8%.
blocking objectionable web content by leveraging multiple information sources. the world wide web has now become a humongous archive of various contents. the inordinate amount of information found on the web presents a challenge to deliver right information to the right users. on one hand, the abundant information is freely accessible to all web denizens; on the other hand, much of such information may be irrelevant or even deleterious to some users. for example, some control and filtering mechanisms are desired to prevent inappropriate or offensive materials such as pornographic websites from reaching children. ways of accessing websites are termed as access scenarios. an access scenario can include using search engines (e.g., image search that has very little textual content), url redirection to some websites, or directly typing (porn) website urls. in this paper we propose a framework to analyze a website from several different aspects or information sources, and generate a classification model aiming to accurately classify such content irrespective of access scenarios. extensive experiments are performed to evaluate the resulting system, which illustrates the promise of the proposed approach.
overview of the acm sigkdd 2004 workshops. following a tradition of several years, kdd conferences host a number of workshops on challenging topics that emerge and demand concerted research activities and intensive sharing of experiences. this short paper an one-glance overview of the 8 workshops that took place at august 22, 2004 before the official opening of kdd 2004 in seattle. the individual workshop reports follow in the next pages. complete information on the workshops including links to the individual sites can be found from the kdd 2004 web site http://www.acm.org/sigs/sigkdd/kdd2004.
closing the gap: automated screening of tax returns to identify egregious tax shelters. according to the most recent strategic plan for the united states internal revenue service (irs), high-income individuals are a primary contributor to the "tax gap," the difference between the amount of tax that should be collected and the amount of tax that actually is collected [1]. this case study addresses the use of machine learning and statistical analysis for the purpose of helping the irs target high-income individuals engaging in abusive tax shelters. kernel-based analysis of known abuse allows targeting individual taxpayers, while associative analysis allows targeting groups of taxpayers who appear to be participating in a tax shelter being promoted by a common financial advisor. unlike many kdd applications that focus on classification or density estimation, this analysis task requires estimating risk, a weighted combination of both the likelihood of abuse and the potential revenue losses.
what are the grand challenges for data mining?: kdd-2006 panel report. we discuss what makes exciting and motivating grand challenge problems for data mining, and propose criteria for a good grand challenge. we then consider possible gc problems from multimedia mining, link mining, large-scale modeling, text mining, and proteomics. this report is the result of a panel held at kdd-2006 conference.
web usage mining: discovery and applications of usage patterns from web data. web usage mining is the application of data mining techniques to discover usage patterns from web data, in order to understand and better serve the needs of web-based applications. web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. this paper describes each of these phases in detail. given its application potential, web usage mining has seen a rapid increase in interest, from both the research and practice communities. this paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. an up-to-date survey of the existing work is also provided. finally, a brief overview of the websift system as an example of a prototypical web usage mining system is given.