In recommender systems, exploring user intents allows for a better understanding and exploration of user preferences, thereby improving recommendation performance. However, existing methods for modeling user intents often do so by statically setting the intent count, which can result in redundancy or insufficiency in capturing the full range of user intents. In order to solve this problem, this paper proposes a model named I ntent-aware Recommendation Based on P rincipal C omponent A nalysis (Intent PCA). Intent PCA is a novel application of PCA in the field of recommender systems. This model defines intents as users’ preferences for some specific relations shown on a knowledge graph, and constructs a user-relation matrix to calculate users’ preferences for relations. Then PCA is applied on the user-relation matrix to extract user intents. Benefit from good characteristics of PCA, our PCA-based user intent extraction model can comprehensively model user intents while simultaneously avoid intent redundancy. Moreover, by combining the user intents, this paper designs an intent-based information propagation method to differentially aggregate information from surrounding neighbor nodes. Experiments conducted on three datasets validate the effectiveness of the proposed Intent PCA model.

Crowdsourcing services provide a fast and cheap way to obtain substantial labeled data by employing crowd workers on the Internet. In crowdsourcing learning, two-stage methods have been widely used, which first infer the integrated label for each instance and then build a learning model using instances with their integrated labels. However, existing two-stage methods mainly focus on how to infer more accurate integrated labels, after that, most of them directly regard the integrated labels as class labels to build a learning model, which loses the detailed worker labeling information in multiple noisy labels and thus results in sub-optimal model accuracy. To solve this problem, in this study, we take the multiple noisy labels of each instance as its attribute value vector to construct another view in addition to the original attribute view, and propose a novel two-stage method called dual-view learning from crowds (DVLFC). In DVLFC, we first pick out workers with sufficient number of labels and augment the multiple noisy label set for each instance, then we build a supervised learning model in each view and at last we fuse their class-membership probabilities to get the final classification result. Extensive experiments on both real-world and artificial crowdsourced datasets prove the effectiveness of DVLFC.

Crowdsourcing provides a cost-effective solution to the problem of obtaining large annotated datasets. In real-world crowdsourcing scenarios, most workers often annotate a few instances only, which results in a significantly sparse crowdsourcing label matrix and subsequently harms the performance of label integration algorithms. Probabilistic matrix factorization (PMF) has been proven to be an effective method for crowdsourcing label completion. However, its low-quality input and output labels limit its performance. To improve its performance, this paper proposes a PMF-based three-stage label completion (PMF-TLC) method. In the first stage, we design a label confidence-based strategy to estimate the quality of each raw label of each worker. Then we flip those low-quality labels in the original crowdsourcing label matrix. In the second stage, we conduct PMF on the flipped label matrix and obtain the completed label matrix with soft labels. In the third stage, we design a between-class margin-based filter to delete those low-quality soft labels in the completed label matrix. Then we convert the remaining high-quality soft labels to hard (logic) labels and obtain the final processed label matrix. Extensive experimental results on real-world and simulated crowdsourced datasets show that PMF-TLC can significantly improve label integration algorithms' performance.

In crowdsourcing scenarios, we can obtain each instance’s multiple noisy labels set from different crowd workers and then use a ground truth inference algorithm to infer its integrated label. Despite the effectiveness of ground truth inference algorithms, a certain level of noise still remains in the integrated labels. To reduce the impact of noise, many noise correction algorithms have been proposed in recent years. To the best of our knowledge, however, nearly all existing noise correction algorithms only exploit each instance’s own multiple noisy label sets but ignore the multiple noisy label sets of its neighbors. Here neighbors refer to the nearest instances found in the feature space based on the distance metric learning. In this article, we propose neighborhood weighted voting-based noise correction (NWVNC). In NWVNC, we at first take advantage of the multiple noisy label sets of each instance’s neighbors (including itself) to estimate the probability that it belongs to its integrated label. Then, we use the estimated probability to identify and filter noise instances and thus obtain a clean set and a noise set. Finally, we train three heterogeneous classifiers on the clean set and correct the noise instances by the consensus voting of three trained classifiers. The experimental results on 34 simulated and two real-world crowdsourced datasets show that NWVNC significantly outperforms all the other state-of-the-art noise correction algorithms used for comparison.

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. Numerous enhancements have been proposed to weaken its attribute conditional independence assumption. However, all of them only focus on the raw attribute view, which is hard to reflect all the data characteristics in real-world applications. To portray data characteristics more comprehensively, in this study, we construct two label views from the raw attributes and propose a novel model called multi-view attribute weighted naive Bayes (MAWNB). In MAWNB, we first build multiple super-parent one-dependence estimators (SPODEs) as well as random trees (RTs), then we utilize each of them to classify each training instance in turn and use all their predicted class labels to construct two label views. Next, to avoid attribute redundancy, we optimize the weight of each attribute value for each class by minimizing the negative conditional log-likelihood (CLL) in each view. Finally, the estimated class-membership probabilities by three views are fused to predict the class label for each test instance. Extensive experiments show that MAWNB significantly outperforms NB and all the other existing state-of-the-art competitors.

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.

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 alleviating 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 a sigmoid transformation of 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.

Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB). In HNB, a hidden parent is created for each attribute which combines the influences from all other attributes. We experimentally test HNB in terms of classification accuracy, using the 36 UCI data sets selected by Weka, and compare it to naive Bayes (NB), selective Bayesian classifiers (SBC), naive Bayes tree (NBTree), tree-augmented naive Bayes (TAN), and averaged one-dependence estimators (AODE). The experimental results show that HNB significantly outperforms NB, SBC, NBTree, TAN, and AODE. In many data mining applications, an accurate class probability estimation and ranking are also desirable. We study the class probability estimation and ranking performance, measured by conditional log likelihood (CLL) and the area under the ROC curve (AUC), respectively, of naive Bayes and its improved models, such as SBC, NBTree, TAN, and AODE, and then compare HNB to them in terms of CLL and AUC. Our experiments show that HNB also significantly outperforms all of them.

Naive Bayes (simply NB) (Langley et al., 1992) has been widely used in machine learning and data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, researchers have made a substantial amount of effort to improve naive Bayes. The related research work can be broadly divided into two approaches: eager learning and lazy learning, depending on when the major computation occurs. Different from eager approach, the key idea for extending naive Bayes from the lazy approach is to learn a naive Bayes for each testing example. In recent years, some lazy extensions of naive Bayes have been proposed. For example, SNNB, LWNB, and LBR. All are aiming at improving the classification accuracy of naive Bayes. In many real-world machine learning and data mining applications, however, an accurate ranking is more desirable than an accurate classification. Responding to this fact, we present a lazy learning algorithm called instance greedily cloning naive Bayes (simply IGCNB) in this paper. Our motivation is to improve naive Bayes' ranking performance measured by AUC (Bradley, 1997; Provost and Fawcett, 1997). We experimentally tested our algorithm, using the whole 36 UCI datasets recommended by Weka, and compared it to C4.4 (Provost and Domingos, 2003), NB (Langley et al., 1992), SNNB (Xie, 2002) and LWNB (Frank, 2003). The experimental results show that our algorithm outperforms all the other algorithms used to compare significantly in yielding accurate ranking.