Naive Bayesian Network Research Articles

베이지안 네트워크 개선을 통한 탐지율 향상의 IDS 모델

In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.

Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network

African highland agro-ecosystems are dominated by small-scale agricultural fields that often contain a mix of annual and perennial crops. This makes such systems difficult to map by remote sensing. We developed an expert Bayesian network model to extract the small-scale coffee fields of Rwanda from very high resolution data. The model was subsequently applied to aerial orthophotos covering more than 99% of Rwanda and on one QuickBird image for the remaining part. The method consists of a stepwise adjustment of pixel probabilities, which incorporates expert knowledge on size of coffee trees and fields, and on their location. The initial naive Bayesian network, which is a spectral-based classification, yielded a coffee map with an overall accuracy of around 50%. This confirms that standard spectral variables alone cannot accurately identify coffee fields from high resolution images. The combination of spectral and ancillary data (DEM and a forest map) allowed mapping of coffee fields and associated uncertainties with an overall accuracy of 87%. Aggregated to district units, the mapped coffee areas demonstrated a high correlation with the coffee areas reported in the detailed national coffee census of 2009 (R2=0.92). Unlike the census data our map provides high spatial resolution of coffee area patterns of Rwanda. The proposed method has potential for mapping other perennial small scale cropping systems in the East African Highlands and elsewhere.

Explaining and Predicting Graduate Employability Using Data-mining Techniques

Data-mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. For a higher education institution with a huge data of graduate employability, data-mining technique is an appropriate for analyzing this information. This study presents a graduate employability model that uses a classification task to identify the most important causes of graduate employability, and to compare the accuracy between Bayesian methods (Naive Bayesian, Averaged One-Dependence Estimators, Averaged One-Dependence Estimators with subsumption resolution, Naive Bayesian Simple, Bayesian networks, and Naive Bayesian Updateable) and Decision tree methods (J48, J48 Graft, Simple Cart, Random Forest, Logical Analysis of Data tree, Rep tree, Decision Stump, and Random tree). The experiments show that 3 attributes with a direct effect on employability are the work province, occupation type, and times find work. The results can provide valuable insights for domain experts. Moreover, the results of this study show that the J48 Graft algorithm, which is a variant of the Decision tree method, had a higher accuracy than the average of other Bayesian algorithms.

Transformer Fault Diagnosis Study Based on Bayesian Case Library

The transformer fault diagnostic method based on Bayesian case library is proposed in the paper. Firstly, a transformer fault case library is established by collecting standard guideline and expert experience. Secondly, by standardizing the states and fault modes in the case library, the method takes the states as inputs and the fault modes as outputs, which are used to train a naive Bayesian network classifier. When it is necessary for a fault diagnosis, the user is expected to input the fault states in order to finalize the correct fault mode with the help of the well-trained classifier. On this basis, and with the details of the fault mode, the method could help to get the fault diagnosis results of the case. Finally, the feasibility and effectiveness of this developed method is illustrated by a numerical example of transformer fault diagnosis on site.

Bayesian prediction of microbial oxygen requirement.

Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.

A Top-down Attention Model Using Naive Bayesian Network

본 논문에서는 탐색하고자 하는 물체의 정보가 되는 특징을 추출하고, 이를 나이브 베이지안 네트워크로 학습하여 물체 탐색 시 바이어싱 정보로 사용하는 하향식 시각주의 모델을 제안한다. 기존의 상향식 시각주의 모델에서는 탐색하고자 하는 물체에 대한 선지식을 사용하지 않기 때문에 탐색하고자 하는 물체가 현저도가 낮을 경우 미탐색되는 한계가 있었다. 본 논문에서 제안하는 모델에서는 학습을 통해 고차원적인 정보를 특징정보로 사용함으로써 상향식 모델에서는 현저도가 낮게 결정되어 미탐색되었던 물체를 탐색할 수 있도록 하였다. 성능 평가를 위하여 특정 물체 탐색 문제에 적용하였으며 기존의 상향식 모델 및 하향식 모델과 비교하였다.

Decision fusion of very high resolution images for urban land-cover mapping based on Bayesian network

Traditional image processing techniques have been proven to be inadequate for urban land-cover mapping using very high resolution (VHR) remotely sensed imagery. Abundant features such as texture, shape, and structural information can be extracted from high-resolution images, which make it possible to distinguish land covers more effectively. However, the multisource characteristics of VHR images place significant demands on the classification method in terms of both efficiency and effectiveness. The most often used method is vector stacking fusion, in which a single classifier is trained over the whole feature space; statistical differences and separability complementarities among different features are rarely considered. Hence, appropriate feature fusion and classification of multisource features become the key issues in the field of urban land-cover mapping. A novel decision fusion method based on a Bayesian network is proposed to handle the multisource features of VHR images which provide redundant or complementary results. Subclassifiers are constructed separately based on multiple feature sets and then embedded into the naive Bayesian network classifier (NBC). The final results are obtained by fusing all the subclassifiers into the NBC framework. Experiments on aerial and QuickBird images demonstrated that the performance of the proposed method is greatly improved compared with vector stacking methods, and significantly improved compared with the multiple-classifier systems and multiple kernels learning support vector machine. Moreover, the proposed method has advantages in feature fusion of VHR images in urban land-cover mapping.

General Bayesian Network Approach to Health Informatics Prediction: Emphasis on Performance Comparison

Health Informatics is emerging as a promising research area. As average life expectancy increases due to medical technology development, health issues remain most sensitive agenda in most of countries in the world. However, heal th technology requires more intelligent mechanisms by which users’ requirement for more accurate prediction about their health problems can be ful- filled. Furthermore, such intelligent mechanisms must provide very flexible and robust procedures by which complicated but necessary decision support functions are allowed. In this sense, this paper proposes General Bayesian Network (GBN) to predict appropriate diets and restaurants that would benefit users’ health. We compared the performance of GBN with other competing techniques such as NBN (naive Bayesian Network), TAN (Tree Augmented naive Bayesian Network), and decision tree. Expe r- iments with real health dataset revealed that GBN results outperform other techniques with statistical validity.

망막 질환 진단을 위한 베이지안 네트워크에 기초한 데이터 분석

본 논문에서 망막 질환 요인간의 의존도 분석을 위해 효율적인 분류기를 활용할 수 있는 방안을 제시하였다. 먼저 여러 베이지안 네트워크 중에서 TAN (Tree-Augmented Naive Bayesian Network), GBN(General Bayesian Network)과 Markov Blanket으로 특징축소된 GBN과의 분류성능과 예측정확률을 비교분석하였다. 그리고 처음으로, 높은 성능을 보인 TAN을 망막 질환 임상데이터의 의존도 분석에 적용하였다. 의존도 분석 결과, 망막 질환의 진단과 예후 예측에 활용의 가능성을 보였다. In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY

The naive Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naive) assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables), the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naive Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naive Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.

Optimization of Dynamic Naive Bayesian Network Classifier with Continuous Attributes

Optimization of Dynamic Naive Bayesian Network Classifier with Continuous Attributes

When Systems Biology Meets Metabolomics: Network Models May Guide Cancer Therapy

Emerging therapeutic strategies have been considered to mediate cancer cell death by regulating oncogene or tumor suppressor-related signaling pathways, as well as to remodel the structure of metabolomics network [7]. A comprehensive knowledge of metabolomics network may provide a basic framework for uncovering the biology of cancer as an integrated system [8]. With increasing metabolomics-wide data, robust mathematical models (i.e., Naive Bayesian network is wellsuited for integrating disparate types of data) seem to be imperative for utilization of metabolomics in cancer therapy [9]. That is to say, traditional ways of studying metabolomics can be complemented by systems biology approaches in current and future cancer therapeutics.

A hierarchical naive Bayesian network classifier embedded GMM for textural image

To address several problems in high-dimensional textures feature space and the deficiencies of the single Gaussian distribution for remote sensing data, this paper proposes a hierarchical naive Bayesian network classifier embedded in a Gaussian mixture model for high-dimensional textural image classification. High-dimensional features are grouped by the model on the basis of the correlations between them. In this way, the high-dimensional problem is decomposed into multiple problems of lower dimension. At the same time, for each group of features, a Gaussian mixture model is applied to simulate the data distribution in feature space for land covers, which fits the “original” data distribution better than a single Gaussian model. The Gaussian mixture model is embedded as a child node into a naive Bayesian network, and then the final classification result is obtained within the naive Bayesian network classifier framework. Experimental results for the classification of Landsat ETM+ and QuickBird image textures demonstrated that the classification accuracy of this method is better than that of a traditional Bayesian network classifier and some other classical classifiers. Comparing with the method dealing with original high-dimensional features, it is also more efficiency and effectiveness with fewer demand of sample size and lower time complexity.

Classification of Movie Reviews Using Complemented Naive Bayesian Classifier

Text classification is an important research area as it enables the computers to work intelligently process unstructured data. This unstructured data is a rich source of information for industries. Most of such opinion rich data (more than 85%) is in text format. In this work we have observed the effect of different machine learning algorithms on the text data including the Naive Bayes. Our main focus is on improving the classification efficiency of Naive Bayes using its complemented version with less sensitivity. The results show that the feature selection procedure from our previous work combined with these algorithms results in significant improvement of classification efficiency and reduced over-fitting compared to the previous work. In many cases our decisions are influenced by the opinions of others. Before the internet awareness became widespread, many of us used to ask our friends or neighbors for opinion of an electronic good or a movie before actually buying it. With the growing availability and popularity of opinion-rich resources such as online review websites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. Unfortunately, 85% of these opinion rich resources are available in unstructured format. It has encouraged the analysts to develop an intelligent system that can automatically categorize or classify these text documents. A lot of research has been carried out, and each of them belongs to one of the following two approaches:  Unsupervised approaches manually derive or impose some rules on the data to extract useful information.  Supervised machine learning approaches use the statistical models such as Naive Bayes, SVM and Bayesian networks etc., The proposed approach in this paper is the supervised approach. Here a series of text documents or reviews which have been previously categorized (manually) and a classifier (model) is trained on these documents. Later this trained classifier is used to categorize new (unclassified) documents. The work documented here is an extension of [2]. Previously, the emphasis was on Pre-Processing i.e. converting these unstructured training documents into structural. Here it is mainly concentrated on classification. The movie-review dataset [1] has been used for experimental purposes. A sequence of pre-processing steps will be carried out to convert these documents into structural format i.e. Term-by-Document matrix (TbyD), as most of the machine learning algorithms are designed to work on structural data rather than on the unstructured data. A set of four good classifiers including the simple and complemented Naive Bayesian, Support Vector Machines (SVM), Bayesian Networks and Discriminative Frequency Estimate with Bayesian Networks are selected for validation. The remainder of this work is presented as follows; section 2 covers the related work done so far to the problem followed by the proposed approach where pre-processing, feature selection and classifiers employed in this work were explained briefly. In the later section 4 and 5 covers the methodology used, the results and observations respectively.

Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network.

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Automatic milking systems (AMS) generate alert lists reporting cows likely to have clinical mastitis (CM). Dutch farmers indicated that they use non-AMS cow information or the detailed alert information from the AMS to decide whether to check an alerted cow for CM. However, it is not yet known to what extent such information can be used to discriminate between true-positive and false-positive alerts. The overall objective was to investigate whether selection of the alerted cows that need further investigation for CM can be made. For this purpose, non-AMS cow information and detailed alert information were used. During a 2-yr study period, 11,156 alerts for CM, including 159 true-positive alerts, were collected at one farm in the Netherlands. Non-AMS cow information on parity, days in milk, season of the year, somatic cell count history, and CM history was added to each alert. In addition, 6 alert information variables were defined. These were the height of electrical conductivity, the alert origin (electrical conductivity, color, or both), whether or not a color alert for mastitic milk was given, whether or not a color alert for abnormal milk was given, deviation from the expected milk yield, and the number of alerts of the cow in the preceding 12 to 96h. Subsequently, naive Bayesian networks (NBN) were constructed to compute the posterior probability of an alert being truly positive based only on non-AMS cow information, based on only alert information, or based on both types of information. The NBN including both types of information had the highest area under the receiver operating characteristic curve (AUC; 0.78), followed by the NBN including only alert information (AUC=0.75) and the NBN including only non-AMS cow information (AUC=0.62). By combining the 2 types of information and by setting a threshold on the computed probabilities, the number of false-positive alerts on a mastitis alert list was reduced by 35%, and 10% of the true-positive alerts would not be identified. To detect CM cases at a farm with an AMS, checking all alerts is still the best option but would result in a high workload. Checking alerts based on a single alert information variable would result in missing too many true-positive cases. Using a combination of alert information variables, however, is the best way to select cows that need further investigation. The effect of adding non-AMS cow information on making a distinction between true-positive and false-positive alerts would be minor.

Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems

Based on sensor measurements, an automatic milking system (AMS) generates mastitis alert lists indicating cows which are likely to have clinical mastitis (CM). Because of the general assumption of equal probabilities of developing CM for all cows, all alerts on the list have the same success rate. As a consequence, it is not possible to rank-order the alerts in terms of their likelihood of CM. In practice, the performance of a CM detection system is not only based on the sensitivity (SN) and specificity (SP) of the system, but is also influenced by the prior probability of a cow having CM. This study illustrates the idea of using cow-specific prior probabilities of CM, based on non-AMS information, to provide a rank-order on the alerts from an AMS. A tree-augmented naive Bayesian network was trained from available data to determine these cow-specific prior probabilities for CM. The graphical structure of the network and the probability tables for its variables in the network were based on data from 274 Dutch dairy herds that recorded each case of CM over an 18-month period. The final data set contained information on a total of 5363 CM cases derived from 28,137 lactations and 22,860 cows. The available prior cow information (parity, days in milk, season of the year, somatic cell count history and CM history) was included as variables in the network. By combining the cow-specific prior probabilities of CM with the SN and SP of the detection system of the AMS, the computed success rates can be used to discriminate between CM alerts. Our illustrations indicate that the success rate might range from 3 to 84%, while assuming an equal overall probability would result in a success rate of 21%. Using the computed success rates, the CM alerts on an alert list can be rank-ordered, thereby providing the dairy farmer information about which cows have the highest priority for visual inspection for CM.

On the Use of Naive Bayesian Classifiers for Detecting Elementary and Coordinated Attacks

Bayesian networks are very powerful tools for knowledge representation and reasoning under uncertainty. This paper shows the applicability of naive Bayesian classifiers to two major problems in intrusion detection: the detection of elementary attacks and the detection of coordinated ones. We propose two models starting with stating the problems and defining the variables necessary for model building using naive Bayesian networks. In addition to the fact that the construction of such models is simple and efficient, the performance of naive Bayesian networks on a representative data is competing with the most efficient state of the art classification tools. We show how the decision rules used in naive Bayesian classifiers can be improved to detect new attacks and new anomalous activities. We experimentally show the effectiveness of these improvements on a recent Web-based traffic. Finally, we propose a naive Bayesian network-based approach especially designed to detect coordinated attacks and provide experimental results showing the effectiveness of this approach.

Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks

Clinical mastitis (CM) can be caused by a wide variety of pathogens and farmers must start treatment before the actual causal pathogen is known. By providing a probability distribution for the causal pathogen, naive Bayesian networks (NBN) can serve as a management tool for farmers to decide which treatment to use. The advantage of providing a probability distribution for the causal pathogen, rather than only providing the most likely causal pathogen, is that the uncertainty involved is visible and a more informed treatment decision can be made. The objective of this study was to illustrate provision of probability distributions for the gram status and for the causal pathogen for CM cases. For constructing the NBN, data were used from 274 Dutch dairy herds in which the occurrence of CM was recorded over an 18-mo period. The data set contained information on 3,833 CM cases. Two-thirds of the data set was used for the construction process and one-third was retained for validation. One NBN was constructed with the CM cases classified according to their gram status, and another was built with the CM cases classified into streptococci, Staphylococcus aureus, or Escherichia coli. Information usually available at a dairy farm was included in both NBN (parity, month in lactation, season of the year, quarter position, SCC and CM history, being sick or not, and color and texture of the milk). Accuracy was calculated to obtain insight in the quality of the constructed NBN. The accuracy of classifying CM cases into gram-positive or gram-negative pathogens was 73%, while the accuracy of classifying CM cases into streptococci, Staph. aureus, or E. coli was 52%. Because only CM cases with a high probability for a single causal pathogen will be considered for pathogen-specific treatment, accuracies based on only classifying CM cases above a particular probability threshold were determined. For instance, for CM cases in which either gram-negative or gram-positive had a probability >0.90, classification according to the gram status reached an accuracy of 97%. We found that the greater the probability for a particular pathogen was for a CM case, the more accurate was the classification of this case as being caused by this pathogen. The probability distributions provided by the NBN and the associated accuracies for varying classification thresholds provide the farmer with considerable insight about the most likely causal pathogen for a CM case and the uncertainty involved.

Classification of Otoneurological Cases According to Bayesian Probabilistic Models

We show that Bayesian methods can be efficiently applied to the classification of otoneurological diseases and to assess attribute dependencies. A set of 38 otoneurological attributes was employed in order to use a naive Bayesian probabilistic model and Bayesian networks with different scoring functions for the classification of cases from six otoneurological diseases. Tests were executed on the basis of tenfold crossvalidation. We obtained average sensitivities of 90%, positive predictive values of 92% and accuracies as high as 97%, which is better than our earlier tests with neural networks. Our assessments indicated that Bayesian methods have good power and potential to classify otoneurological patient cases correctly even if this is often a complicated task for the best specialists. Bayesian methods classified the current medical data and knowledge well.