Due to its simplicity, efficiency and efficacy, naive Bayes (NB) has continued to be one of the top 10 algorithms in the data mining and machine learning community. Of numerous approaches to alleviate its conditional independence assumption, feature weighting has placed more emphasis on highly predictive features than those that are less predictive. In this paper, we argue that for NB highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for NB. In CFW, the weight for a feature is proportional to the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model. Besides, we apply CFW to text classification and have achieved remarkable improvements.

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy.

In crowdsourcing scenarios, we can often obtain each instance's multiple noisy labels from different crowd workers and then use a label integration method to infer its integrated label. In spite of the effectiveness of label integration methods, a certain level of label noise still exists in integrated labels. To reduce the impact of label noise, noise correction has attracted much attention from researchers, and therefore a certain number of noise correction methods were proposed in recent years. Among them, Between-class Margin-based Noise Correction (BMNC) has demonstrated remarkable denoising performance. However, BMNC can only handle binary classification tasks. For multiclass classification tasks, we propose an effective but very simple noise correction method in this paper. We refer to our proposed method as Label Distribution-based Noise Correction (LDNC). At first, LDNC transforms each instance's multiple noisy labels into a label distribution. Then, LDNC uses the margin between the first- and second-largest label probabilities in the label distribution to identify and filter each possible noise instance and thus obtains a clean set and a noise set. Finally, LDNC builds a classifier on the clean set to relabel all instances in the noise set. The experimental results on 16 simulated and one real-world multiclass crowdsourced data sets show that LDNC significantly outperforms all the other existing state-of-the-art noise correction methods.