A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes

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.

Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.

Improved Age Estimation Mechanism from Medical Data Based on Deep Instance Weighting Fusion

Age estimation is very useful in the fields of pattern recognition and data mining, especially for medical problems. The current methods of age estimation do not consider the relationships among instances, especially the internal hierarchical structure, which limits the potential improvement of the age estimation error. A deep age estimation mechanism based on deep instance weighting fusion is proposed to solve this problem. First, an iterative means clustering (IMC) algorithm is designed to construct the hierarchical instance space (multiplelayer instance space) and obtain multiple trained regression models. Second, a deep instance weighting fusion (DIWF) mechanism is designed to fuse the results from the trained regression models to produce the final results. The experimental results show that the mean absolute error (MAE) of the estimated ages can be decreased significantly on two publicly available data sets, with relative gains of 4.97% and 0.8% on the Heart Disease Data Set and Diabetes Mellitus Data Set, respectively. Additionally, some factors that may influence the performance of the proposed mechanism are studied. In general, the proposed age estimation mechanism is effective. In addition, the mechanism is not a concrete algorithm but framework algorithm (or mechanism), and can be used to generate various concrete age estimation algorithms, so the mechanism is helpful for related studies.