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Using Causal Information and Local Measures to Learn Bayesian Networks
, 1993
"... In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the ..."
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Cited by 37 (2 self)
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In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make significant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local refinement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size.
Tractable learning of large bayes net structures from sparse data
, 2004
"... statistics for creating the global Bayes Net. This paper addresses three questions. Is it useful to attempt to learn a Bayesian network structure with hundreds of thousands of nodes? How should such structure search proceed practically? The third question arises out of our approach to the second: ho ..."
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Cited by 34 (4 self)
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statistics for creating the global Bayes Net. This paper addresses three questions. Is it useful to attempt to learn a Bayesian network structure with hundreds of thousands of nodes? How should such structure search proceed practically? The third question arises out of our approach to the second: how can Frequent Sets (Agrawal et al., 1993), which are extremely popular in the area of descriptive data mining, be turned into a probabilistic model? Large sparse datasets with hundreds of thousands of records and attributes appear in social networks, warehousing, supermarket transactions and web logs. The complexity of structural search made learning of factored probabilistic models on such datasets unfeasible. We propose to use Frequent Sets to significantly speed up the structural search. Unlike previous approaches, we not only cache nway sufficient statistics, but also exploit their local structure. We also present an empirical evaluation of our algorithm applied to several massive datasets.
A characterization of the Dirichlet distribution through global and local parameter independence
 THE ANNALS OF STATISTICS
, 1997
"... ..."
Causal discovery via MML
 IN: PROCEEDINGS OF THE THIRTEENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1996
"... Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed ..."
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Cited by 21 (10 self)
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Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate re ections of the original models and compare favorably with those of TETRAD II (Spirtes et al. 1994) even when it is supplied with prior temporal information and MML is not.
Formalising a software safety case via belief networks
, 1995
"... The SHIP project (ref. EV5V 103) is being carried out with financial support from the EEC in the ..."
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Cited by 18 (9 self)
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The SHIP project (ref. EV5V 103) is being carried out with financial support from the EEC in the
BiasCorrected Bootstrap and Model Uncertainty
"... The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and realworld data demonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical models. ..."
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Cited by 12 (2 self)
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The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and realworld data demonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical models.
Learning hybrid Bayesian networks from data
, 1998
"... We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first method ..."
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Cited by 11 (1 self)
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We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original nondiscretized data for the parameterization of the learned structure. The second methodology uses nondiscretized data both to learn the Bayesian network structure and its parameterization. For the direct handling of continuous data, we propose the use of artificial neural networks as probability estimators, to be used as an integral part of the scoring metric defined to search the space of Bayesian network structures. With both methodologies, we assume the availability of a complete dataset, with no missing values or hidden variables. We report experimental results aimed at comparing the two methodologies. These results provide evidence that learning with discretized data presents advantages both in terms of efficiency and in terms of accuracy of the learned models over the alternative approach of using nondiscretized data.
Opcode sequences as representation of executables for dataminingbased unknown malware detection, Information Sciences 227
, 2013
"... Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, sig ..."
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Cited by 11 (0 self)
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Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, signaturebased detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. Therefore, the signaturebased method consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting unknown malware.
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
, 2003
"... When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this inf ..."
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Cited by 9 (0 self)
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When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis.