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. INDEX TERMS Naive Bayes, attribute weighting, weight adjustment, classification.

To address the problem of DC bus voltage surge caused by load demand fluctuation in an off-grid microgrid, here, an adaptive energy optimization method based on a hybrid energy-storage system to maintain the stability of DC bus voltage is presented. The adaptive energy optimization method consists of three parts: the average filtering algorithm, extracting fluctuating power in demand load; the supercapacitor terminal voltage control, keeping the terminal voltage of the supercapacitor near reference; and the battery pack balance control, adjusting the charge/discharge to balance the state of charge for battery packs. In this proposed method, after extracting the fluctuating power by the low-pass filter when the demand load fluctuates, the battery packs release the power to offset the low-frequency fluctuation load and the supercapacitor to instantaneously compensate the high-frequency fluctuation power, to prolong the service life of batteries and maintain the stability of DC bus voltage. The effectiveness of the proposed adaptive energy optimization method is validated and is confirmed to maintain the stable operation of the off-grid microgrid, extend the cycle life of batteries in off-grid microgrid simulations and experiments.

Link prediction is an important problem in network data mining, which is dedicated to predicting the potential relationship between nodes in the network. Normally, network link prediction based on supervised classification will be trained on a dataset consisting of a set of positive samples and a set of negative samples. However, well-labeled training datasets with positive and negative annotations are always inadequate in real-world scenarios, and the datasets contain a large number of unlabeled samples that may hinder the performance of the model. To address this problem, we propose a positive-unlabeled learning framework with network representation for network link prediction only using positive samples and unlabeled samples. We first learn representation vectors of nodes using a network representation method. Next, we concatenate representation vectors of node pairs and then feed them into different classifiers to predict whether the link exists or not. To alleviate data imbalance and enhance the prediction precision, we adopt three types of positive-unlabeled (PU) learning strategies to improve the prediction performance using traditional classifier estimation, bagging strategy and reliable negative sampling. We conduct experiments on three datasets to compare different PU learning methods and discuss their influence on the prediction results. The experimental results demonstrate that PU learning has a positive impact on predictive performances and the promotion effects vary with different network structures.

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.