The Software for Cultures and the Cultures in Software * (2008)
H. R. Hansen, M. Bichler, H. Harald (eds, Gregory E. Kersten, Stan Matwin, Sunil J. Noronha, ...
Abstract- Software is viewed as an artefact which interacts with cultures of societies in which it functions. On the one hand, software manufacturers make efforts to adapt the appearance of their...
Privacy in Data Mining Using Formal Methods (2008)
Stan Matwin, Amy Felty, István Hernádvölgyi, Venanzio Capretta
Abstract. There is growing public concern about personal data collected by both private and public sectors. People have very little control over what kinds of data are stored and how such data is...
Thomas Gärtner, John W. Lloyd, Peter A. Flach, Stan Matwin
Abstract. This paper brings together two strands of machine learning of increasing importance: kernel methods and highly structured data. We propose a general method for constructing a kernel...
A Framework for Comparative Evaluation of Classifiers in the Presence of Class Imbalance (2008)
William Elazmeh, Nathalie Japkowicz, Stan Matwin
Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of...
Evaluating Misclassifications in Imbalanced Data (2008)
William Elazmeh, Nathalie Japkowicz, Stan Matwin
Abstract. Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the...
RSKT07 Chairs RSFDGrC07 Chairs JRS07 Publicity Chairs (2008)
Setsuo Ohsuga, York U, Setsuo Ohsuga, Lotfi A. Zadeh, Dominik Slezak, Guoyin Wang, ...
includes different concepts and approaches to information technologies. As a result, JRS07 covers a very wide area of information science. It is an exciting attempt because information is itself a...
A Pattern Language for Providing Client-Server Confidential Communication (2008)
This paper extracts and documents patterns that identify problems and solutions concerning confidentiality in a client-server environment. These patterns are then organized as a pattern language. The...
This paper shows how machine learning can help in analyzing and understanding historical change. Using data from the Canadian census of 1901, we discover the influences on bilingualism in Canada at...
Inferring and Revising Theories with Confidence: Analyzing the 1901 Canadian Census (2008)
This paper shows how machine learning can help historians ana-lyze and understand important social phenomena. Using data from the Canadian census of 1901, we discover the influences on bilingual-ism...
Privacy Compliance Enforcement in Email (2008)
Quintin Armour, William Elazmeh, Nour El-kadri, Nathalie Japkowicz, Stan Matwin
Abstract. Privacy is one of the main societal concerns raised by critics of the uncontrolled growth and spread of information technology in developed societies. The purpose of this paper is to...
Privacy in Data Mining Using Formal Methods (2008)
Stan Matwin, Amy Felty, István Hernádvölgyi, Venanzio Capretta
Abstract. There is growing public concern about personal data collected by both private and public sectors. People have very little control over what kinds of data are stored and how such data is...
Privacy-preserving collaborative association rule mining (2008)
Justin Zhan, Stan Matwin, Liwu Chang
Abstract. This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among parties involved...
Private Mining of Association Rules (2008)
Justin Zhan, Stan Matwin, Liwu Chang
Abstract. This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parties...
Privacy in Data Mining Using Formal Methods (2008)
Stan Matwin, Amy Felty, István Hernádvölgyi, Venanzio Capretta
Abstract. There is growing public concern about personal data collected by both private and public sectors. People have very little control over what kinds of data are stored and how such data is...
Case Authoring from Text and Historical Experiences (2008)
Marvin Zaluski, Nathalie Japkowicz, Stan Matwin
Abstract. The problem of repair and maintenance of complex systems, such as aircraft, cars and trucks is certainly a nontrivial task. Maintenance technicians must use a great amount of knowledge and...
Machine Learning, 57, 233--269, 2004 (2008)
Naive Bayesian Classification, Peter A. Flach, Stan Matwin
In this paper we present 1BC and 1BC2,two systems that perform naive Bayesian classification of structured individuals. The approach of 1BC is to project the individuals along first-order features....
Using Lexical Knowledge in Text Classification (2007)
Stan Matwin, Sam Scott, Sam Scott
This paper describes several experiments in text classification using WordNet, a rich source of lexical background knowledge available in the public domain. WordNet is used to map the original words...
Maria Fernandacnandad, Stan Matwin, Fabrizio Sebastiani
In this work we investigate the usefulness of n-grams for document indexing in text categorization (TCi We call-gram a set g k of n word stems, and we say that g k occurs in a document d j when a...
Research Issues Arising in Applying Machine Learning to Oil Slick Detection (2007)
Miroslav Kubat, Robert Holte, Stan Matwin
Applications in image processing and remote sensing raise questions that have so far received only marginal attention from the machine learning community. And yet, each of our issues, we believe,...
A Normalization Method For Contextual Data: Experience From A Large-Scale Application (2007)
Sylvain Létourneau, Stan Matwin, Fazel Famili
. The paper describes a pre-processing technique to normalize contextually-dependent data before appling Lachine Learning algorithm. Unlike many previous methods, our approach to normalization does...
David G. Goodenough, Daniel Charlebois, Stan Matwin, Nigel Daley
As part of the Applied Information Systems Research Program sponsored by NASA, a System of Experts for Intelligent Data Management, SEIDAM, has been created. As a component of SEIDAM, a case-based...
An Explainable-Induction Approach for Diagnosing Retinal Degeneration (2007)
Stan Matwin, Riverson Rios, Jim Mount
. In many medical domains in which one is to apply Artificial Intelligence-based methods for the analysis of available data, two entities exist: the domain theory and the data. Traditional machine...
Evaluating Data Mining Models: A Pattern Language (2007)
Jerffeson Souza, Stan Matwin, Nathalie Japkowicz
This paper extracts and documents patterns that identify recurring solutions for the problem of evaluation of data mining models. The five patterns presented in this paper are organized as a pattern...
Abstract Email Classification with Co-Training (2007)
Svetlana Kiritchenko, Stan Matwin
The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training- an algorithm that uses...
Inferring and Revising Theories with Confidence: Analyzing the 1901 Canadian Census (2007)
Chris Drummond, Stan Matwin, Pack Kaelbling
This paper shows how machine learning can help historians analyze and understand important social phenomena. Using data from the Canadian census of 1901, we discover the influences on bilingualism in...
A Pattern Language for Providing Client-Server Confidential Communication (2007)
This paper extracts and documents patterns that identify problems and solutions concerning confidentiality in a client-server environment. These patterns are then organized as a pattern language. The...
(ML93), Using Qualitative Models to Guide Inductive Learning (2007)
This paper presents a method for using qualitative modds to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the...
A GENERAL CRITERION FOR MEASURING (2007)
F. Bergadano, S. Matwin, R. S. Michalski, J. Zhang, Francesco Bergadano, Stan Matwin, ...
An important aspect of any learning method is an evaluation of the learned knowledge, in particular, an evaluation of the plausibility and usefulness of concept descriptions that are being created....
Statistical Phrases in Automated Text Categorization (2007)
Maria Fern, A Caropreso, Stan Matwin, Fabrizio Sebastiani
In this work we investigate the usefulness of n-grams for document indexing in text categorization (TC). We call n-gram a set t k of n word stems, and we say that t k occurs in a document d j when a...
Explanation-based Learning Helps Acquire Knowledge from Natural Language Texts (2007)
Sylvain Delisle, Stan Matwin, Ong Wang, Lionel Zupan
Existing systems to acquire knowledge from expository texts do not perform any learning beyond interpreting the contents of the text. The opportunity to learn from examples included in texts is not...
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity (2006)
Nadeau, David, Turney, Peter D., Matwin, Stan
In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as...
Automatic Dream Sentiment Analysis (2006)
Nadeau, David, Sabourin, Catherine, De Koninck, Joseph, Matwin, Stan, Turney, Peter D.
In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges...
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity (2006)
Nadeau, David, Turney, Peter D., Matwin, Stan
In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as...
Automatic Dream Sentiment Analysis (2006)
Nadeau, David, Sabourin, Catherine, De Koninck, Joseph, Matwin, Stan, Turney, Peter D.
In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges...
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity (2006)
Nadeau, David, Turney, Peter D., Matwin, Stan
In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as...
Automatic Dream Sentiment Analysis (2006)
Nadeau, David, Sabourin, Catherine, De Koninck, Joseph, Matwin, Stan, Turney, Peter D.
In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges...
Unsupervised named-entity recognition: Generating gazetteers and resolving ambiguity (2006)
David Nadeau, Peter D. Turney, Stan Matwin
Abstract. In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention...
Automatic Dream Sentiment Analysis (2006)
Council Canada, Turney P, David Nadeau, Catherine Sabourin, Joseph De Koninck, Stan Matwin, ...
de l'information
Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity (2006)
Council Canada, David Nadeau, Peter D. Turney, Stan Matwin
de l'information
Relational learning of biological networks (2005)
Combe, Cyril, Schächter, Vincent, Matwin, Stan, D'Alché-Buc, Florence
The last few years have seen a lot of different approaches for the reconstruction of biological networks. Those approaches essentially differ about the kind of data used and about the models of...
Privacy-sensitive information flow with JML (2005)
Guillaume Dufay, Amy Felty, Stan Matwin
Abstract. In today’s society, people have very little control over what kinds of personal data are collected and stored by various agencies in both the private and public sectors. We describe an...
PEEP - Privacy Enforcement in Email Project (2005)
Narjes Boufaden William, William Elazmeh, Stan Matwin, Nathalie Japckowicz
Breaching information privacy is a critical problem where legal remedies intervene only after the fact rather than prevent it. This paper presents an organizational privacy compliance engine that...
Functional annotation of genes using hierarchical text categorization (2005)
Svetlana Kiritchenko, Stan Matwin, A. Fazel Famili
This paper addresses the task of functional annotation of genes from biomedical literature. We view this task as a hierarchical text categorization problem with Gene Ontology as a class hierarchy. We...
Privacy-sensitive information flow with JML (2005)
Guillaume Dufay, Amy Felty, Stan Matwin
Abstract. In today’s society, people have very little control over what kinds of personal data are collected and stored by various agencies in both the private and public sectors. We describe an...
Privacy-preserving collaborative sequential pattern mining (2004)
Justin Z. Zhan, Liwu Chang, Stan Matwin
In the modern business world, collaborative data mining becomes especially important because of the mutual benefit it brings to the collaborators. During the collaboration, each party of the...
Machine Learning, 57, 305--333, 2004 (2004)
Compact Representation Of, Jan Struyf, Jan Ramon, Maurice Bruynooghe, Sofie Verbaeten, Hendrik Blockeel, ...
In many applications of Inductive Logic Programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory....
Email classification with temporal features (2004)
Svetlana Kiritchenko, Stan Matwin, Suhayya Abu-hakima
Abstract. We propose a novel solution to the email classification problem: the integration of temporal information with the traditional content-based classification approaches. We discover temporal...
Filtering multi-instance problems to reduce dimensionality in relational learning (2004)
Erick Alphonse, Lri Bât, Stan Matwin
Abstract. Attribute-value based representations, standard in today’s data mining systems, have a limited expressiveness. Inductive Logic Programming provides an interesting alternative,...
Mining the Software Change Repository of a Legacy Telephony (2004)
Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin
Ability to predict whether a change in one file may require a change in another can be extremely helpful to a software maintainer. Software change repositories store historic changes applied to a...
Integrating Guidance into Relational Reinforcement Learning (2004)
Abstract. Reinforcement learning, and Q-learning in particular, encounter two major problems when dealing with large state spaces. First, learning the Q-function in tabular form may be infeasible...
Email classification with temporal features (2004)
Svetlana Kiritchenko, Stan Matwin, Suhayya Abu-hakima
Abstract. We propose a novel solution to the email classification problem: the integration of temporal information with the traditional content-based classification approaches. We discover temporal...
Privacy-preserving data mining in electronic surveys (2004)
Electronic surveys are an important resource in data mining. However, how to protect respondents ’ data privacy during the survey is a challenge to the security and privacy community. In this...
Privacy-oriented data mining by proof checking (2002)
Abstract. This paper shows a new method which promotes ownership of data by people about whom the data was collected. The data owner may preclude the data from being used for some purposes, and allow...
Feature Subset Selection and Inductive Logic Programming (2002)
Erick Alphonse Alphonse, Stan Matwin
Applicability of ILP to real-world problems is constrained by the high dimensionality of ILP tasks. This paper proposes to reduce the dimensionality of the ILP example space by bringing feature...
Inferring and revising theories with confidence: data mining the 1901 Canadian census (2002)
Chris Drummond, Stan Matwin, Chad Gaffield
To address this problem, we apply a data-mining algorithm to the 1901 Canadian census. For the first time, the census asked all residents in Canada three
Supporting Software Maintenance by Mining Software Update Records (2001)
Jelber Sayyad Shirabad, Shirabad Timothy, C. Lethbridge, Stan Matwin
This paper describes the application of inductive methods to data extracted from both source code and software maintenance records. We would like to extract relations that indicate which files in, a...
The Software for Cultures and the Cultures in Software (2000)
Gregory E. Kersten, Stan Matwin, Sunil J. Noronha, Mik A. Kersten
Abstract-Software is viewed as an artifact which interacts with cultures of societies in which it functions. Software manufacturers make efforts to adapt the appearance of their products to aesthetic...
A Generic Architecture for Knowledge Acquisition Tools in Cardiology (2000)
Athia Maral De, Antônio A. Ximenes, Stan Matwin, Guilherme Travassos, Ana Regina Rocha
. Knowledge-acquisition is well known to be a bottleneck activity in the development of knowledge-based systems. Several techniques and tools were proposed to support this process. However, knowledge...
Feature engineering for text classification (1999)
Most research in text classification has used the “bag of words ” representation of text. This paper examines some alternative ways to represent text based on syntactic and semantic relationships...
Machine Learning Method for Software Quality Model Building (1999)
Software quality prediction can be cast as a concept learning problem. In this paper, we discuss the full cycle of an application of Machine Learning to software quality prediction. As it often...
Feature Engineering for Text Classification (1999)
Most research in text classification has used the "bag of words" representation of text. This paper examines some alternative ways to represent text based on syntactic and semantic...
The Software for Cultures and the Cultures in Software (1999)
Gregory E. Kersten, Stan Matwin, Sunil J. Noronha, Mik A. Kersten
Software is viewed as an artifact which interacts with cultures of societies in which it functions. Software manufacturers make efforts to adapt the appearance of their products to aesthetic and...
Data Mining For Prediction of Aircraft Component Replacement (1999)
Sylvain Létourneau, Fazel Famili, Stan Matwin
The operation and maintenance of modern sensor-equipped systems such as passenger aircraft generate vast amounts of numerical and symbolic data. Learning models from this data to predict problems...
Machine Learning for the Detection of Oil Spills in Satellite Radar Images (1998)
Miroslav Kubat Robert, Robert C. Holte, Stan Matwin
. During a project examining the use of machine learning techniques for oil spill detection, we have encountered several essential questions that we believe deserve the attention of the research...
Machine Learning for the Detection of Oil Spills in Satellite Radar Images (1998)
Miroslav Kubat, Robert Holte, Stan Matwin
. During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the researchcommunity....
Experiments with Learning Parsing Heuristics (1998)
Sylvain Delisle, Sylvain LETOURNEAU, Stan Matwin
Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" into the software in...
The Design of a Configurable Text Summarization System (1998)
Ken Barker, Barker Yllias Chali, Terry Copeck, Stan Matwin, Stan Szpakowicz
This report presents the design of a flexible summarization system consisting of several independent linguistic processing tools that can be rapidly configured and extensively parameterized....
Machine Learning for the Detection of Oil Spills in Satellite Radar Images (1998)
Miroslav Kubat, Robert C. Holte, Stan Matwin, Ron Kohavi, Foster Provost
During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community....
Machine Learning for the Detection of Oil Spills in Satellite Radar Images (1998)
Miroslav Kubat, Robert C. Holte, Stan Matwin
. During a project examining the use of machine learning techniques for oil spill detection, we have encountered several essential questions that we believe deserve the attention of the research...
Text Classification Using WordNet Hypernyms (1998)
This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms. Six binary classification tasks of varying difficulty are...
The Design of a Configurable Text Summarization System (1998)
Ken Barker, Yllias Chali, Terry Copeck, Stan Matwin, Stan Szpakowicz
This report presents the design of a flexible summarization system consisting of several independent linguistic processing tools that can be rapidly configured and extensively parameterized....
Using Lexical Knowledge in Text Classification (1998)
Stan Matwin, Text Classification, Background Knowledge, Sam Scott, Sam Scott
This paper describes several experiments in text classification using WordNet, a rich source of lexical background knowledge available in the public domain. WordNet is used to map the original words...
Learning when Negative Examples Abound (1997)
Miroslav Kubat Robert, Robert Holte, Stan Matwin
. Existing concept learning systems can fail when the negative examples heavily outnumber the positive examples. The paper discusses one essential trouble brought about by imbalanced training sets...
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection (1997)
Adding examples of the majority class to the training set can have a detrimental effect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm...
Discovering Useful Knowledge from Aircraft Operation/Maintenance Data (1997)
Sylvain Létourneau, A. Famili, Stan Matwin
In this paper we present an overview of our research in discovering useful knowledge from data acquired during the operation and maintenance of a fleet of commercial aircraft. In particular, we...
Discovering Useful Knowledge from Aircraft Operation/Maintenance Data (1997)
Sylvain Tourneau, A. Famili, Stan Matwin
In this paper we present an overview of our research in discovering useful knowledge from data acquired during the operation and maintenance of a fleet of commercial aircraft. In particular, we...
The Role of Context in Concept Learning (1996)
Many practical, real-life applications of concept learning are impossible to address without taking into consideration the background of the concept, its frame of reference, and the particual...
Peter Clark, Cao Feng, Stan Matwin, Ko Fung
. Evidence for image classification can be considered to come from two sources: traditional statistical information derived algorithmically from image data, and modelbased evidence arising from...
A WordNet-based Algorithm for Word Sense Disambiguation (1995)
Xiaobin Li, Stan Szpakowicz, Stan Matwin
We present an algorithm for automatic word sense disambiguation, based on lexical knowledge contained in WordNet and on the results of surface-syntactic analysis. The algorithm is part of a system...
Explainable Induction with an Imperfect Qualitative Model (1995)
This paper addresses the problem of learning concept descriptions that are interpretable, or explainable. Explainability is understood as the ability to justify the learned concept in terms of the...
From Text to Horn Clauses: Combining Linguistic Analysis and (1994)
Sylvain Delisle, Ken Barker, Jean-françois Delannoy, Stan Matwin, Stan Szpakowicz
The paper describes a system that extracts knowledge from technical English texts. Our basic assumption is that in technical texts syntax is a reliable indication of meaning. Consequently, semantic...
Inverting Implication with Small Training Sets (1994)
David W. Aha, Stephane Lapointe, Charles X. Ling, Stan Matwin
. We present an algorithm for inducing recursive clauses using inverse implication (rather than inverse resolution) as the underlying generalization method. Our approach applies to a class of logic...
Maintainability: Factors and Criteria (1994)
Marc Frappier, Stan Matwin, Ali Mili
This memorandum is the first deliverable. Its objectives are to define the concept of maintainability, to describe the factors influencing it and to define criteria by which maintainability can be...
Learning Recursive Relations with Randomly Selected Small Training Sets (1994)
David Aha, Stephane Lapointe, Charles X. Ling, Stan Matwin
We evaluate CRUSTACEAN, an inductive logic programming algorithm that uses inverse implication to induce recursive clauses from examples. This approach is well suited for learning a class of...
From Text to Horn Clauses: Combining Linguistic Analysis and (1994)
Sylvain Delisle, Ken Barker, Jean-françois Delannoy, Stan Matwin, Stan Szpakowicz
The paper describes a system that extracts knowledge from technical English texts. Our basic assumption is that in technical texts syntax is a reliable indication of meaning. Consequently, semantic...
From Text to Horn Clauses: Combining Linguistic Analysis and (1994)
Sylvain Delisle, Ken Barker, Jean-françois Delannoy, Stan Matwin, Stan Szpakowicz
The paper describes a system that extracts knowledge from technical English texts. Our basic assumption is that in technical texts syntax is a reliable indication of meaning. Consequently, semantic...
Learning Domain Theories Using Abstract Background Knowledge (1993)
Abstract. Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such...
Learning Domain Theories Using Abstract Background Knowledge (1993)
Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain...
Using Qualitative Models to Guide Inductive Learning (1993)
This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the...
Learning Singly-Recursive Relations from Small Datasets (1993)
David W. Aha, Charles X. Ling, Stan Matwin, Stephane Lapointe
The inductive logic programming system LOPSTER was created to demonstrate the advantage of basing induction on logical implication rather than `-subsumption. LOPSTER's sub-unification procedures...
Using qualitative models to guide inductive learning (1993)
This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the...
Learning Domain Theories using Abstract Background Knowledge (1992)
Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain...
Learning Domain Theories using Abstract Background Knowledge (1992)
Background Knowledge Peter Clark and Stan Matwin Ottawa Machine Learning Group Computer Science, University of Ottawa Ontario, CANADA K1N 6N5 fpclark,stang@csi.uottawa.ca Abstract Substantial machine...
Learning Two-tiered Descriptions of Flexible Concepts: The POSEIDON System (1991)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System (1990)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions (1989)
Bergadano, Francesco, Matwin, Stan, Zhang, Jianping, Michalski, Ryszard S.
A General Criterion for Measuring Quality of Concept Descriptions (1988)
Bergadano, Francesco, Matwin, Stan, Michalski, Ryszard S., Zhang, Jianping
Measuring Quality of Concept Descriptions (1988)
Bergadano, Francesco, Matwin, Stan, Zhang, Jianping, Michalski, Ryszard S.
A Normalization Method For Contextual Data: Experience From A Large-Scale Application
Sylvain Letourneau, Stan Matwin, Fazel Famili
. This paper describes a pre-processing technique to normalize contextually-dependent data before applying Machine Learning algorithms. Unlike many previous methods, our approach to normalization...
Engineering of a Clinical Decision Support Framework for the Point of Care Use
Wilk, Szymon, Michalowski, Wojtek, O’Sullivan, Dympna, Farion, Ken, Matwin, Stan
Computerized decision support for use at the point of care has to be comprehensive. It means that clinical information stored in electronic health records needs to be integrated with various forms of...