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## Bayesian Network Classifiers (1997)

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Citations: | 779 - 20 self |

### Citations

12166 |
Elements of information theory
- Cover, Thomas
- 1991
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Citation Context ...i) =0. Hence, we need to maximize the term X i,⇡(i)>0 I ˆ PD (Ai; A ⇡(i),C)+ X i,⇡(i)=0 I ˆ PD (Ai; C) . (8) We simplify this term by using the identity known as the chain law for mutual information (=-=Cover & Thomas, 1991-=-): IP (X; Y, Z) =IP (X; Z)+IP (X; Y|Z). Hence, we can rewrite expression (8) as X IPD ˆ (Ai; C)+ X IPD ˆ (Ai; A⇡(i)|C) i i,⇡(i)>0 Note that the first term is not affected by the choice of ⇡(i). Theref... |

10437 | Introduction to Algorithms
- Cormen, Leiserson, et al.
- 1992
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Citation Context ...he sum of weights attached to the selected arcs is maximized. There are well-known algorithms for solving this problem of time complexity O(n 2 log n), where n is the number of vertices in the graph (=-=Cormen et al., 1990-=-). The Construct-Tree procedure of CL consists of four steps: 1. Compute I ˆ PD (Xi; Xj) between each pair of variables, i 6= j, where IP (X; Y) = X P (x, y) P (x, y) log x,y P (x)P (y) is the mutual ... |

8746 |
Probabilistic Reasoning in Intelligent Systems
- Pearl
- 1988
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Citation Context ...ence? In order to tackle this problem effectively, we need an appropriate language and efficient machinery to represent and manipulate independence assertions. Both are provided by Bayesian networks (=-=Pearl, 1988-=-). These networks are directed acyclic graphs that allow efficient and effective representation of the joint probability distribution over a set of random variables. Each vertex in the graph represent... |

6460 |
C4.5: Programs for machine learning
- Quinlan
- 1993
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Citation Context ...7% without smoothing. The complete results for the smoothed version of naive Bayes are reported in Table 3. Given that TAN performs better than naive Bayes and that naive Bayes is comparable to C4.5 (=-=Quinlan, 1993-=-), a state-of-the-art decision tree learner, we may infer that TAN should perform rather well in comparison to C4.5. To confirm this prediction, we performed experiments comparing TAN to C4.5, and als... |

4769 | Pattern Classification and scene analysis - Duda, Hart - 1973 |

2144 |
On information and sufficiency
- Kullback, Leibler
- 1951
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Citation Context ...maximizing the log likelihood we are minimizing the description of D. Another way of viewing this optimization process is to use cross entropy, which is also known as the Kullback-Leibler divergence (=-=Kullback & Leibler, 1951-=-). Cross entropy is a measure of distance between two probability distributions. Formally, D(P (X)||Q(X)) = X x2Val(X) P (x) log P (x) Q(x) . (A.1) One information-theoretic interpretation of cross en... |

1524 |
Modeling by shortest data description
- Rissanen
- 1978
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Citation Context ...eiger et al., 1996). An in-depth discussion of the pros and cons of each scoring function is beyond the scope of this paper. Henceforth, we concentrate on the MDL scoring function. The MDL principle (=-=Rissanen, 1978-=-) casts learning in terms of data compression. Roughly speaking, the goal of the learner is to find a model that facilitates the shortest description of the original data. The length of this descripti... |

1516 | Wrappers for Feature Subset Selection - Kohavi, John - 1997 |

1437 |
Pattern recognition and neural networks
- Ripley
- 1996
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Citation Context ...58 N. FRIEDMAN, D. GEIGER, AND M. GOLDSZMIDT assess only the second term, since it is the only one relevant to the classification process. In general, neither of these approaches dominates the other (=-=Ripley, 1996-=-). The naive Bayesian classifier and the extensions we have evaluated belong to the sampling paradigm. Although the unrestricted Bayesian networks (described in Section 3) do not strictly belong in ei... |

1368 | A Bayesian method for the induction of probabilistic networks from data
- Cooper, Herskovits
- 1992
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Citation Context ...efficient algorithms in Section 4.1, where we propose a particular extension to naive Bayes. The two main scoring functions commonly used to learn Bayesian networks are the Bayesian scoring function (=-=Cooper & Herskovits, 1992-=-; Heckerman et al., 1995), and the function based on the principle of minimal description length (MDL) (Lam & Bacchus, 1994; Suzuki, 1993); see also Friedman and Goldszmidt (1996c) for a more recent a... |

1241 | A study of cross-validation and bootstrap for accuracy estimation and model selection - Kohavi |

1132 | Learning Bayesian networks: The combination of knowledge and statistical data
- Heckerman, Geiger, et al.
- 1995
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Citation Context ...tion 4.1, where we propose a particular extension to naive Bayes. The two main scoring functions commonly used to learn Bayesian networks are the Bayesian scoring function (Cooper & Herskovits, 1992; =-=Heckerman et al., 1995-=-), and the function based on the principle of minimal description length (MDL) (Lam & Bacchus, 1994; Suzuki, 1993); see also Friedman and Goldszmidt (1996c) for a more recent account of this scoring f... |

1120 | Optimal Statistical Decisions - Groot - 1970 |

859 | Approximating discrete probability distribution with dependence trees
- Chow, Liu
- 1968
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Citation Context ...ed one by choosing a root variable and setting the direction of all edges to be outward from it. CL prove that this procedure finds the tree that maximizes the likelihood given the data D. Theorem 1 (=-=Chow & Liu, 1968-=-) Let D be a collection of N instances of X1,...,Xn. The Construct-Tree procedure constructs a tree BT that maximizes LL(BT |D) and has time complexity O(n 2 · N).142 N. FRIEDMAN, D. GEIGER, AND M. G... |

841 |
UCI repository of machine learning databases
- Murphy, Aha
- 1994
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Citation Context ...lassifiers based on unrestricted networks) to that of the naive Bayesian classifier. We ran this experiment onBAYESIAN NETWORK CLASSIFIERS 139 25 data sets, 23 of which were from the UCI repository (=-=Murphy & Aha, 1995-=-). Section 5 describes in detail the experimental setup, evaluation methods, and results. As the results in Figure 2 show, the classifier based on unrestricted networks performed significantly better ... |

819 | Multi-interval discretization of continuous-valued attributes for classification learning - Fayyad, Irani - 1993 |

640 |
Inference and missing data
- Rubin
- 1976
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Citation Context ...works but we leave this issue for future work. Regarding the problem of missing values, in theory, probabilistic methods provide a principled solution. If we assume that values are missing at random (=-=Rubin, 1976-=-), then we can use the marginal likelihood (the probability assigned to the parts of the instance that were observed) as the basis for scoring models. If the values are not missing at random, then mor... |

531 | Supervised and unsupervised discretization of continuous features - Dougherty, Kohavi, et al. - 1995 |

487 | Estimating Continuous Distributions in Bayesian Classifiers - John, Langley - 1995 |

429 | An Analysis of Bayesian Classifiers
- Langley, Iba, et al.
- 1992
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Citation Context ... of conditional probability, we get Pr(C|A1,...,An) =↵ · Pr(C) · Q n i=1 Pr(Ai|C), where ↵ is a normalization constant. This is in fact the definition of naive Bayes commonly found in the literature (=-=Langley et al., 1992-=-). The problem of learning a Bayesian network can be informally stated as: Given a training set D = {u1,...,uN } of instances of U, find a network B that best matches D. The common approach to this pr... |

358 | Social networks
- Wellman, Wasserman
- 1998
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Citation Context ... size increases; furthermore, they are both asymptotically correct: with probability equal to one the learned distribution converges to the underlying distribution as the number of samples increases (=-=Heckerman, 1995-=-; Bouckaert, 1994; Geiger et al., 1996). An in-depth discussion of the pros and cons of each scoring function is beyond the scope of this paper. Henceforth, we concentrate on the MDL scoring function.... |

353 | Beyond independence: Conditions for the optimality of the simple Bayesian classifier
- Domingos, Pazzani
- 1996
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Citation Context ...actors such as numeric attributes and missing values. 6.1. Related work on naive Bayes There has been recent interest in explaining the surprisingly good performance of the naive Bayesian classifier (=-=Domingos & Pazzani, 1996-=-; Friedman, 1997a). The analysis provided by Friedman (1997a) is particularly illustrative, in that it focuses on characterizing how the bias and variance components of the estimation error combine to... |

271 | Learning bayesian networks with local structure - Friedman, Goldszmidt - 1999 |

261 | Induction of selective Bayesian classifiers
- Langley, Sage
- 1994
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Citation Context ...e may infer that TAN should perform rather well in comparison to C4.5. To confirm this prediction, we performed experiments comparing TAN to C4.5, and also to the selective naive Bayesian classifier (=-=Langley & Sage, 1994-=-; John & Kohavi, 1997). The latter approach searches for the subset of attributes over which naive Bayes has the best performance. The results, displayed in Figures 5 and 6 and in Table 2, show that T... |

256 |
The EM algorithm for graphical association models with missing data
- Lauritzen
- 1995
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Citation Context ...written as the sum of local terms (as in Equation 4). Moreover, to evaluate the optimal choice of parameters for a candidate network structure, we must perform nonlinear optimization using either EM (=-=Lauritzen, 1995-=-) or gradient descent (Binder et al., 1997). The problem of selecting the best structure is usually intractable in the presence of missing values. Several recent efforts (Geiger et al., 1996; Chickeri... |

248 | Theory refinement on Bayesian networks - Buntine - 1991 |

247 | Learning Bayesian belief networks. An approach based on the MDL principle
- Lam, Bacchus
- 1994
(Show Context)
Citation Context ...didate network. We start by examining a straightforward application of current Bayesian networks techniques. We learn networks using the score based on the minimum description length (MDL) principle (=-=Lam & Bacchus, 1994-=-; Suzuki, 1993), and use them for classification. The results, which are analyzed in Section 3, are mixed: although the learned networks perform significantly better than naive Bayes on some data sets... |

244 | On bias, variance, 0/1-loss, and the curse-of-dimensionality
- Friedman
- 1997
(Show Context)
Citation Context ...ributes and missing values. 6.1. Related work on naive Bayes There has been recent interest in explaining the surprisingly good performance of the naive Bayesian classifier (Domingos & Pazzani, 1996; =-=Friedman, 1997-=-a). The analysis provided by Friedman (1997a) is particularly illustrative, in that it focuses on characterizing how the bias and variance components of the estimation error combine to influence class... |

219 | Learning Bayesian networks is NP-complete
- Chickering
- 1996
(Show Context)
Citation Context ...oring function that evaluates each network with respect to the training data, and then to search for the best network according to this function. In general, this optimization problem is intractable (=-=Chickering, 1995-=-). Yet, for certain restricted classes of networks, there are efficient algorithms requiring polynomial time in the number of variables in the network. We indeed take advantage of these efficient algo... |

217 | Bayesian analysis in expert systems - Spiegelhalter, Dawid, et al. - 1993 |

201 | A guide to the literature on learning Probabilistic Networks from data - Buntine - 1996 |

194 | Efficient approximations for the marginal likelihood of bayesian networks with hidden variables
- Chickering, Heckerman
- 1997
(Show Context)
Citation Context ...en, 1995) or gradient descent (Binder et al., 1997). The problem of selecting the best structure is usually intractable in the presence of missing values. Several recent efforts (Geiger et al., 1996; =-=Chickering & Heckerman, 1996-=-) have examined approximations to the marginal score that can be evaluated efficiently. Additionally, Friedman (1997b) has proposed a variant of EM for selecting the graph structure thatBAYESIAN NETW... |

192 | Estimating probabilities: A crucial task in machine learning - Cestnik - 1990 |

176 | Adaptive probabilistic networks with hidden variables
- Binder, Koller, et al.
- 1997
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Citation Context ...mplies that, to maximize the choice of parameters for a fixed network structure, we must resort to search methods such as gradient descent over the space of parameters (e.g., using the techniques of (=-=Binder et al., 1997-=-)). When learning the network structure, this search must be repeated for each structure candidate, rendering the method computationally expensive. Whether we can find heuristic approaches that will a... |

150 | Probabilistic similarity networks
- Heckerman
- 1990
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Citation Context ...fic class, that is, ˆ PD(A1,...,An | C = ci). The Bayesian network for ci is called a local network for ci. The set of local networks combined with a prior on C, P (C), is called a Bayesian multinet (=-=Heckerman, 1991-=-; Geiger & Heckerman, 1996). Formally, a multinet is a tuple M = hPC,B1,...,Bki where PC is a distribution on C, and Bi is a Bayesian network over A1,...,An for 1 apple i apple k = |Val(C)|. A multine... |

145 | Learning belief networks in the presence of missing values and hidden variables
- Friedman
- 1997
(Show Context)
Citation Context ...ributes and missing values. 6.1. Related work on naive Bayes There has been recent interest in explaining the surprisingly good performance of the naive Bayesian classifier (Domingos & Pazzani, 1996; =-=Friedman, 1997-=-a). The analysis provided by Friedman (1997a) is particularly illustrative, in that it focuses on characterizing how the bias and variance components of the estimation error combine to influence class... |

122 |
Semi-naive bayesian classifier
- Kononenko
- 1991
(Show Context)
Citation Context ...tal results (see Figure 6) show that the methods we examine here are usually more accurate than the selective naive Bayesian classifier as used by John and Kohavi (1997). Work in the second category (=-=Kononenko, 1991-=-; Pazzani, 1995; Ezawa & Schuermann, 1995) are closer in spirit to our proposal, since they attempt to improve the predictive accuracy by removing some of the independence assumptions. The semi-naive ... |

104 |
Knowledge representation and inference in similarity networks and Bayesian multinets. Artif. Intell
- Geiger, Heckerman
- 1996
(Show Context)
Citation Context ...s, ˆ PD(A1,...,An | C = ci). The Bayesian network for ci is called a local network for ci. The set of local networks combined with a prior on C, P (C), is called a Bayesian multinet (Heckerman, 1991; =-=Geiger & Heckerman, 1996-=-). Formally, a multinet is a tuple M = hPC,B1,...,Bki where PC is a distribution on C, and Bi is a Bayesian network over A1,...,An for 1 apple i apple k = |Val(C)|. A multinet M defines a joint distri... |

100 | MLC++: A machine learning library in C
- Kohavi, John, et al.
- 1994
(Show Context)
Citation Context ...-5 24 vote 16 2 435 CV-5 25 waveform-21 21 3 300 4700 The accuracy of each classifier is based on the percentage of successful predictions on the test sets of each data set. We used the MLC++ system (=-=Kohavi et al., 1994-=-) to estimate the prediction accuracy for each classifier, as well as the variance of this accuracy. Accuracy was measured via the holdout method for the larger data sets (that is, the learning proced... |

92 | Building classifiers using Bayesian networks - Friedman, Goldszmidt - 1996 |

78 | Discretization of continuous attributes while learning Bayesian networks - Friedman, Goldszmidt - 1996 |

74 | Searching for dependencies in Bayesian classifiers
- Pazzani
- 1995
(Show Context)
Citation Context ...Figure 6) show that the methods we examine here are usually more accurate than the selective naive Bayesian classifier as used by John and Kohavi (1997). Work in the second category (Kononenko, 1991; =-=Pazzani, 1995-=-; Ezawa & Schuermann, 1995) are closer in spirit to our proposal, since they attempt to improve the predictive accuracy by removing some of the independence assumptions. The semi-naive Bayesian classi... |

54 | Efficient learning of selective Bayesian network classifiers - Singh, Provan - 1995 |

52 | Learning Bayesian networks: a unification for discrete and Gaussian domains - Heckerman, Geiger - 1995 |

39 |
A construction of Bayesian networks from databases based on an MDL scheme
- Suzuki
- 1993
(Show Context)
Citation Context ...art by examining a straightforward application of current Bayesian networks techniques. We learn networks using the score based on the minimum description length (MDL) principle (Lam & Bacchus, 1994; =-=Suzuki, 1993-=-), and use them for classification. The results, which are analyzed in Section 3, are mixed: although the learned networks perform significantly better than naive Bayes on some data sets, they perform... |

34 | Approximating Probability Distributions to Reduce Storage Requirements. Information and Control - Lewis - 1959 |

32 | An entropy-based learning algorithm of Bayesian conditional trees - Geiger - 1992 |

31 |
Wrappers for Feature Subset Selection. Artif Intelligence 97(1-2
- Kohavi, John
- 1997
(Show Context)
Citation Context ...hould perform rather well in comparison to C4.5. To confirm this prediction, we performed experiments comparing TAN to C4.5, and also to the selective naive Bayesian classifier (Langley & Sage, 1994; =-=John & Kohavi, 1997-=-). The latter approach searches for the subset of attributes over which naive Bayes has the best performance. The results, displayed in Figures 5 and 6 and in Table 2, show that TAN is competitive wit... |

27 | A comparison of induction algorithms for selective and non-selective Bayesian classifiers
- Singh, GM
- 1995
(Show Context)
Citation Context ... combine several feature subset selection strategies with an unsupervised Bayesian network learning routine. This procedure, however, can be computationally intensive (e.g., some of their strategies (=-=Singh & Provan, 1995-=-) involve repeated calls to a the Bayesian network learning routine). 6.2. The conditional log likelihood Even though the use of log likelihood is warranted by an asymptotic argument, as we have seen,... |

26 | Properties of diagnostic data distributions - DAWID - 1976 |

24 |
Fraud/uncollectible debt detection using a Bayesian network based learning system: A rare binary outcome with mixed data structures
- Ezawa, Schuermann
- 1995
(Show Context)
Citation Context ...that the methods we examine here are usually more accurate than the selective naive Bayesian classifier as used by John and Kohavi (1997). Work in the second category (Kononenko, 1991; Pazzani, 1995; =-=Ezawa & Schuermann, 1995-=-) are closer in spirit to our proposal, since they attempt to improve the predictive accuracy by removing some of the independence assumptions. The semi-naive Bayesian classifier (Kononenko, 1991) is ... |

11 |
Properties of Bayesian network learning algorithms
- Bouckaert
- 1994
(Show Context)
Citation Context ...furthermore, they are both asymptotically correct: with probability equal to one the learned distribution converges to the underlying distribution as the number of samples increases (Heckerman, 1995; =-=Bouckaert, 1994-=-; Geiger et al., 1996). An in-depth discussion of the pros and cons of each scoring function is beyond the scope of this paper. Henceforth, we concentrate on the MDL scoring function. The MDL principl... |

6 | Efficient learning of selective Bayesian network classifiers - Provan - 1996 |

5 | A comparison of induction algorithms for selective and non-selective Bayesian classifiers - Provan - 1995 |

4 |
Asymptotic model selection for directed graphs with hidden variables
- Geiger, Heckerman, et al.
- 1996
(Show Context)
Citation Context ... are both asymptotically correct: with probability equal to one the learned distribution converges to the underlying distribution as the number of samples increases (Heckerman, 1995; Bouckaert, 1994; =-=Geiger et al., 1996-=-). An in-depth discussion of the pros and cons of each scoring function is beyond the scope of this paper. Henceforth, we concentrate on the MDL scoring function. The MDL principle (Rissanen, 1978) ca... |

3 | On bias, variance - Friedman - 1997 |

1 | Semi-naiveBayesian classifier - Kononenko - 1991 |