Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on numerical attributes. At the same time, none of them takes into account the correlations among instances. To fill this gap, we propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB). Specifically, it first uses original attributes to construct an instance correlation graph (ICG) to represent the correlations among instances. Then, it employs a variational graph auto-encoder (VGAE) to generate new attributes from the constructed ICG and uses them to augment original attributes.Finally, it weights each augmented attribute to alleviate the attribute redundancy and builds GNB on the weighted attributes. The experimental results on tens of datasets show that ICGNB significantly outperforms its deserved competitors.Our codes and datasets are available at https://github.com/jiangliangxiao/ICGNB.

采用实例加权的隐朴素贝叶斯算法对教育数据进行分析和预测.通过对教育数据进行分析和预测,采用属性值频率加权的方法提升了模型准确性.通过计算不同属性值所占的属性值频率对实例进行加权,实例的权重表示了不同的实例对实验结果的影响.实验使用了UCI提供的葡萄牙中学学生成绩数据集,对比了多种贝叶斯算法,并经过十次十折交叉验证方法验证模型性能.准确率、召回率、AUC指标和F-score指标的实验结果表明,IWHNB算法在649个实例的数据集上表现最佳,展现出优异的分类性能.

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011.

Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, reduces its classification performance. Of the numerous approaches to alleviating its assumption of the conditional independence of features, structure extension has attracted less attention from researchers. To the best of our knowledge, only structure-extended MNB (SEMNB) has been proposed so far. SEMNB averages all weighted super-parent one-dependence multinomial estimators; therefore, it is an ensemble learning model. In this paper, we propose a single model called hidden MNB (HMNB) by adapting the well-known hidden NB (HNB). HMNB creates a hidden parent for each feature, which synthesizes all the other qualified features’ influences. For HMNB to learn, we propose a simple but effective learning algorithm without incurring a high-computational-complexity structure-learning process. Our improved idea can also be used to improve complement NB (CNB) and the one-versus-all-but-one model (OVA), and the resulting models are simply denoted as HCNB and HOVA, respectively. The extensive experiments on eleven benchmark text classification datasets validate the effectiveness of HMNB, HCNB, and HOVA.

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

•Many different categories of approaches have been proposed to improve naive Bayes.•Few works simultaneously pay attention to attribute weighting and instance weighting.•We propose attribute and instance weighted naive Bayes (AIWNB) in this paper.•To learn AIWNB, we propose an eager and a lazy algorithms: AIWNBE and AIWNBL.•The experimental results validate the effectiveness of the proposed algorithms.

In the field of data mining and machine learning, classification is a key problem to which the Bayesian network model is frequently applied due to its simplicity and high efficiency.As a classical Bayesian network model for semi-supervised learning,One-Dependence Estimator(ODE) has been widely concerned by researchers.However,the existing ODE model classifiers do not consider the varying contribution of different attribute nodes acting as root nodes to the classification process.Therefore,this paper combines ODE model classifier with the attribute value weighting method,and on this basis proposes the MI-ODE algorithm.The algorithm adopts Mutual Information(MI) to measure the dependence between attribute values and class variables of the attribute root node,which is used as the weight of the ODE model.Then weighted average is implemented for the attribute values of the ODE classifier model.The MI-ODE algorithm is tested on 36 standard data sets for real-world classification problems,and results show that compared with NB algorithm,AODE algorithm and TAN algorithm,the proposed algorithm has better classification performance.

Naive Bayes (NB) is one of the top 10 data mining algorithms. However, its assumption of conditional independence rarely holds true in real-world applications. To alleviate this assumption, numerous attribute weighting approaches have been proposed. However, few of these simultaneously pay attention to the horizontal granularity of attribute values and vertical granularity of class labels. In this study, we propose a new paradigm for fine-grained attribute weighting, named class-specific attribute value weighting. For each class, this approach discriminatively assigns a specific weight to each attribute value. We refer to the resulting improved model as class-specific attribute value weighted NB (CAVWNB). In CAVWNB, the class-specific attribute value weight matrix is learned by either maximizing the conditional log-likelihood (CLL) or minimizing the mean squared error (MSE). Thus, two versions are proposed, which we denote as CAVWNBCLL and CAVWNBMSE, respectively. Extensive experimental results on a large number of datasets show that both CAVWNBCLL and CAVWNBMSE significantly outperform NB and all the other existing state-of-the-art attribute weighting approaches used for comparison.

Naive Bayes makes an assumption regarding conditional independence, but this assumption rarely holds true in real-world applications, so numerous attempts have been made to relax this assumption. However, to the best of our knowledge, few studies have assigned different weights to different attribute values. In this study, we propose a new paradigm for a simple, efficient, and effective attribute value weighting approach called the correlation-based attribute value weighting approach (CAVW), which assigns a different weight to each attribute value by computing the difference between the attribute value-class correlation (relevance) and the average attribute value-attribute value intercorrelation (average redundancy). In CAVW, we use the information theoretic method with a strong theoretical background to assign different weights to different attribute values. Two different attribute value weighting measures called the mutual information (MI) measure and the Kullback–Leibler (KL) measure are employed, and thus two different versions are created, which we denote as CAVW-MI and CAVW-KL, respectively. According to extensive empirical studies based on a collection of 36 benchmark datasets from the University of California at Irvine repository, CAVW-MI and CAVW-KL both obtained more satisfactory experimental results compared with the naive Bayesian classifier and other four existing attribute weighting methods, and our methods also maintain the simplicity of the original naive Bayes model.

Due to its simplicity, efficiency and efficacy, naive Bayes (NB) continues to be one of the top 10 data mining algorithms. A mass of improved approaches to NB have been proposed to weaken its conditional independence assumption. However, there has been little work, up to the present, on instance weighting filter approaches to NB. In this paper, we propose a simple, efficient, and effective instance weighting filter approach to NB. We call it attribute (feature) value frequency-based instance weighting and denote the resulting improved model as attribute value frequency weighted naive Bayes (AVFWNB). In AVFWNB, the weight of each training instance is defined as the inner product of its attribute value frequency vector and the attribute value number vector. The experimental results on 36 widely used classification problems show that AVFWNB significantly outperforms NB, yet at the same time maintains the computational simplicity that characterizes NB.

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants. Attribute weighting approaches can be broadly divided into two categories: filters and wrappers. In this paper, we mainly focus on wrapper attribute weighting approaches because they have generally higher classification performance than filter attribute weighting approaches. We propose a weight adjusted naive Bayes approach and simply denote it WANB. In WANB, the importance of each attribute in the classification of a training data set is learned and the weight vector reflecting this importance is updated. We use weight adjustment based on objective functions to find the optimal weight vector. We compare WANB with standard NB and its state-of-the-art attribute weighting approaches. Empirical studies on a collection of 36 benchmark datasets show that the classification performance of WANB significantly outperforms NB and all the existing filter approaches used to compare. Yet at the same time, compared to the existing wrapper approach called DEWANB, WANB is much more efficient and comprehensible.

•Almost all existing attribute weighting approaches to naive Bayes are class-independent.•We propose a new class-specific attribute weighting paradigm for naive Bayes.•The resulting model is called class-specific attribute weighted naive Bayes (CAWNB).•To learn CAWNB, we propose two gradient-based learning algorithms.•The experimental results validate the effectiveness of the proposed algorithms.

Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison.