Publications

Background This paper presents a novel free-text information retrieval system designed especifically for medical case finding applications. Methods The proposed system begins by extracting medical information from free-text narrative reports and storing it in a predefined relational clinical data mart. The extraction is performed using a sophisticated medical thesaurus with a fast regular expression based method for pattern match. Following the extraction phase, inclusion/exclusion criteria are provided to the system using a physician-friendly user interface. The system converts the entered criteria into a single SQL command, which can be then executed on the relational data mart. Results Using inclusion and exclusion criteria of a published diabetes clinical trial (Diabetes Care 25:30–34, 2002) on a 500-discharge summaries database of a large hospital in NY, the performance of the system has been compared with the results obtained through a manual search by two physicians. (see Table) Conclusions The initial comparison validates that the proposed system can be successfully used for an ultra rapid case-finding applications, such as identifying appropriate patients to be enrolled in clinical trials with a high sensitivity, while paying only a small price for reduced specificity. Clinical Pharmacology & Therapeutics (2005) 77, P13–P13; doi: 10.1016/j.clpt.2004.11.054 Sensitivity Specificity Time Needed MDs search 71% 100% 50 hours System search 98% 91% 15 seconds

In this paper, we introduce a novel approach to generate an intention prediction model of user interactions with systems. As part of this new approach, we include personal aspects, such as user characteristics, that can increase prediction accuracy. The model is automatically trained according to the user's fixed attributes (e.g., demographic data such as age and gender) and the user's sequences of actions in the system. The generated model has a tree structure. The building blocks of each node can be any probabilistic sequence model [such as hidden Markov models (HMMs) and conditional random fields (CRFs)] and each node is split according to user attributes. Thus, we refer to this algorithm as an attribute-driven model tree. The new model was first tested on simulated data in which users with different attributes (such as age and gender) behave differently when trying to accomplish various tasks. We then validated the ability of the algorithm to discover the relevant attributes. We tested our algorithm on two real datasets: from a web application and a mobile application dataset. The results were encouraging and indicate the capability of the proposed method to discover the correct user intention model and increasing intention prediction accuracy compared with single HMM or CRF models.

Many information systems use XML documents to store data and to interact with other systems. Abnormal documents, which can be the result of either an on-going cyber attack or the actions of a benign user, can potentially harm the interacting systems and are therefore regarded as a threat. In this paper we address the problem of anomaly detection and localization in XML documents using machine learning techniques. We present XML-AD – a new XML anomaly detection framework. Within this framework, an automatic method for extraction of feature from XML documents as well as a practical method for transforming XML features into vectors of fixed dimensionality was developed. With these two methods in place, the XML-AD framework makes it possible to utilize general learning algorithms for anomaly detection. The core of the framework consists of a novel multi-univariate anomaly detection algorithm, ADIFA. The framework was evaluated using four XML documents datasets which were obtained from real information systems. It achieved over 89% true positive detection rate with less than 0.2% of false positives.

Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.

Honey pots are computer resources that are used to detect and deflect network attacks on a protected system. The data collected from honey pots can be utilized to better understand cyber-attacks and provide insights for improving security measures, such as intrusion detection systems. In recent years, attackers' sophistication has increased significantly, thus additional and more advanced analytical models are required. In this paper we suggest several unique methods for detecting attack propagation patterns using Markov Chains modeling and complex networks analysis. These methods can be applied on attack datasets collected from honey pots. The results of these models shed light on different attack profiles and interaction patterns between the deployed sensors in the honey pot system. We evaluate the suggested methods on a massive data set which includes over 167 million observed attacks on a globally distributed honey pot system. Analyzing the results reveals interesting patterns regarding attack correlations between the honey pots. We identify central honey pots which enable the propagation of attacks, and present how attack profiles may vary according to the attacking country. These patterns can be used to better understand existing or evolving attacks, and may aid security experts to better deploy honey pots in their system.

Groups may need assistance in reaching a joint decision. Elections can reveal the winning item, but this means the group members need to vote on, or at least consider all available items. Our challenge is to minimize the amount of preferences that need to be elicited and thus reduce the effort required from the group members. We present a model that offers a few innovations. First, rather than offering a single winner, we propose to offer the group the best top-k alternatives. This can be beneficial if a certain item suddenly becomes unavailable, or if the group wishes to choose manually from a few selected items. Secondly, rather than offering a definite winning item, we suggest to approximate the item or the top-k items that best suit the group, according to a predefined confidence level. We study the tradeoff between the accuracy of the proposed winner item and the amount of preference elicitation required. Lastly, we offer to consider different preference aggregation strategies. These strategies differ in their emphasis: towards the individual users (Least Misery Strategy) or towards the majority of the group (Majority Based Strategy). We evaluate our findings on data collected in a user study as well as on real world and simulated datasets and show that selecting the suitable aggregation strategy and relaxing the termination condition can reduce communication cost up to 90%. Furthermore, the commonly used Majority strategy does not always outperform the Least Misery strategy. Addressing these three challenges contributes to the minimization of preference elicitation in expert systems.

No abstract is provided for this article.

Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.

We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. Dimensionality reduction methods represent datasets using a small number of attributes while preserving the information conveyed by the original dataset. The ensemble members are trained based on dimension-reduced versions of the training set. These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters. This construction meets both the diversity and accuracy criteria which are required to construct an ensemble classifier where the former criterion is obtained by the various input parameter values and the latter is achieved due to the decorrelation and noise reduction properties of dimensionality reduction. In order to classify a test sample, it is first embedded into the dimension reduced space of each individual classifier by using an out-of-sample extension algorithm. Each classifier is then applied to the embedded sample and the classification is obtained via a voting scheme. We present three variations of the proposed approach based on the Random Projections, the Diffusion Maps and the Random Subspaces dimensionality reduction algorithms. We also present a multi-strategy ensemble which combines AdaBoost and Diffusion Maps. A comparison is made with the Bagging, AdaBoost, Rotation Forest ensemble classifiers and also with the base classifier which does not incorporate dimensionality reduction. Our experiments used seventeen benchmark datasets from the UCI repository. The results obtained by the proposed algorithms were superior in many cases to other algorithms.

In real-world machine learning applications, there is a cost associated with sampling of different features. Budgeted learning can be used to select which feature-values to acquire from each instance in a dataset, such that the best model is induced under a given constraint. However, this approach is not possible in the domain of online learning since one may not retroactively acquire feature-values from past instances. In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream. In this paper we introduce the issue of online budgeted learning and describe a general framework for addressing this challenge. We propose two types of feature value acquisition policies based on the multi-armed bandit problem: random and adaptive. Adaptive policies perform online adjustments according to new information coming from a data stream, while random policies are not sensitive to the information that arrives from the data stream. Our comparative study on five real-world datasets indicates that adaptive policies outperform random policies for most budget limitations and datasets. Furthermore, we found that in some cases adaptive policies achieve near-optimal results.