An analysis of two probabilistic model induction techniques

Crawford, Stuart L.; Fung, Robert

doi:10.1007/978-1-4899-4537-2_16

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…For a more detailed discussion, see (Crawford and Fung, 1991). Classification trees can readily handle both discrete and continuous variables.…”

Section: Classification Treesmentioning

confidence: 99%

“…Belief networks can capture the probabilistic relationships among multiple variables, without the need to designate a classification variable. These networks provide a natural representation for capturing causal relationships among a set of variables (see (Crawford & Fung, 1991) for a case study). In addition, inference algorithms exist for computing the probability of any subset of variables conditioned on the values of any other subset.…”

Section: Classification Treesmentioning

confidence: 99%

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A Bayesian method for the induction of probabilistic networks from data

Cooper¹,

Herskovits²

1992

Mach Learn

2,446

2,038

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Abstract. This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabifistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probahilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.Keywords. probabilistic networks, Bayesian belief networks, machine learning, induction I n t r o d u c t i o nIn this paper, we present a Bayesian method for constructing a probabilistic network from a database of records, which we call cases. Once constructed, such a network can provide insight into probabilistic dependencies that exist among the variables in the database. One application is the automated discovery of dependency relationships. The computer program searches for a probabilistic-network structure that has a high posterior probability given the database, and outputs the structure and its probability. A related task is computer-assisted hypothesis testing: The user enters a hypothetical structure of the dependency relationships among a set of variables, and the program calculates the probability of the structure given a database of cases on the variables.We can also construct a network and use it for computer-based diagnosis. For example, suppose we have a database in which a case contains data about the behavior of some system (i.e., findings). Suppose further that a case contains data about whether this particular behavior follows from proper system operation, or alternatively, is caused by one of several possible faults. Assume that the database contains many such cases from previous episodes of proper and faulty behavior. The method that we present in this paper can be used to construct from the database a probabilistic network that captures the probabilistic dependencies among findings and faults. Such a network then can be applied to classify future cases of system behavior by assigning a posterior probability to each of the possible faults and to the event "proper system operation." In this paper, we also shall discuss diagnostic inference that is based on combining the inferences of multiple alternative networks.

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“…For a more detailed discussion, see (Crawford and Fung, 1991). Classification trees can readily handle both discrete and continuous variables.…”

Section: Classification Treesmentioning

confidence: 99%

Section: Classification Treesmentioning

confidence: 99%

A Bayesian method for the induction of probabilistic networks from data

Cooper¹,

Herskovits²

1992

Mach Learn

2,446

2,038

View full text Add to dashboard Cite

show abstract

“…feedback process Rocchio, 1971). The main Crawford, Fung, and their coworkers (Crawford, Fung, assumption behind relevance feedback (RF) is that docu-Appelbaum, & Tong, 1991;Crawford & Fung, 1992; ments relevant to a particular query resemble each other in Fung & Crawford, 1990) have developed a probabilistic the sense that they are represented by reasonably similar induction technique called CONSTRUCTOR and have vectors of keywords or descriptors (Salton, 1989). This compared it with the popular CART algorithm (Breiman, implies that if a retrieved document has been identified Friedman, Olshen, & Stone, 1984).…”

Section: Learning Systems For Irmentioning

confidence: 99%

A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing

Chen

Shankaranarayanan

She

et al. 1998

J. Am. Soc. Inf. Sci.

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Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge‐based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution‐based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000‐record COMPEN database was performed to examine the performances of these inductive query‐by‐example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out‐perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze users' preferred documents (or records), identify users' underlying information needs, and also suggest alternatives for search for database management systems and Internet applications. © 1998 John Wiley & Sons, Inc.

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“…Fuhr et al (1990) adopted regressions methods and ID3 for their feature-based automatic indexing technique. Crawford, Fung, and their coworkers (Crawford et al, 199 I;Crawford & Fung, 1992: Fung & Crawford, 1990) have developed a probabilistic induction technique called CONSTRUCTOR and have compared it with the popular CART algorithm (Breiman et al, 1984). Their experiment showed that CON-STRUCTOR's output is more interpretable than that produced by CART, but CART can be applied to more situations (e.g., real-valued training sets).…”

Section: Knowledge-based Systems In Irmentioning

confidence: 99%

Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms

Chen

1995

J. Am. Soc. Inf. Sci.

197

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Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 198Os, knowledge-based techniques also made an impressive contribution to "intelligent" information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.

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An analysis of two probabilistic model induction techniques

Cited by 3 publications

References 4 publications

A Bayesian method for the induction of probabilistic networks from data

A Bayesian method for the induction of probabilistic networks from data

A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing

Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms

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