Intelligent Data Analysis: An International Journal

Time series forecasting has many practical applications in a variety of domains such as commerce, finance, medicine, weather, environment, and transportation. There exist so many methods developed for time series forecasting. However, most of the forecasting methods do not pay attention to anomalies in time series even though time series are sensitive to anomalies. Anomaly patterns cause negative effects on the accuracy of time series forecasting. In this paper, we propose a novel anomaly repair-based approach to improve time series forecasting in the case of anomaly existence. In our approach, an effective time series forecasting framework, EPL_S_X, is proposed with anomaly smoothing as a pre-processing stage and any existing time series prediction algorithm X. In particular, our proposed approach consists of three steps including detecting anomalies, repairing anomalies by using our smoothing method, and forecasting time series using preprocessed time series. Experimental results on several time series datasets reveal that our proposed approach improves remarkably the accuracy of many existing time series forecasting methods. It also outperforms the two robust time series forecasting methods that are based on exponential and Holt-Winters smoothing. With such better prediction performance, our approach is not only more effective but also more useful when dealing with anomalies in time series forecasting.

With the rapid development of social media and mobile Internet, short reviews, such as Weibo and Twitter, have exploded online. Discovering topics from short reviews is significant for many practical applications. It can effectively not only identify users’ attitudes and emotions but also enhance customer satisfaction and shopping experience. Because reviews are relatively short, the sparsity of reviews considerably restricts the quality of topic discovery. To improve the efficiency of topic discovery, we introduce the concept of data enhancement and strengthen the data in sentences and words in short reviews based on the weight of importance. We then propose a topic model for reviews to topic discovery based on data enhancement (shorted as DE-LDA). We verify the rationality and feasibility of DE-LDA on real datasets. Results show that the proposed method outperforms benchmarks in topic discovery and also has better clustering effects.

Software maintainability is a significant contributor while choosing particular software. It is helpful in estimation of the efforts required after delivering the software to the customer. However, issues like imbalanced distribution of datasets, and redundant and irrelevant occurrence of various features degrade the performance of maintainability prediction models. Therefore, current study applies ImpS algorithm to handle imbalanced data and extensively investigates several Feature Selection (FS) techniques including Symmetrical Uncertainty (SU), RandomForest filter, and Correlation-based FS using one open-source, three proprietaries and two commercial datasets. Eight different machine learning algorithms are utilized for developing prediction models. The performance of models is evaluated using Accuracy, G-Mean, Balance, & Area under the ROC Curve. Two statistical tests, Friedman Test and Wilcoxon Signed Ranks Test are conducted for assessing different FS techniques. The results substantiate that FS techniques significantly improve the performance of various prediction models with an overall improvement of 18.58%, 129.73%, 80.00%, and 45.76% in the median values of Accuracy, G-Mean, Balance, & AUC, respectively for all the datasets taken together. Friedman test advocates the supremacy of SU FS technique. Wilcoxon Signed Ranks test showcases that SU FS technique is significantly superior to the CFS technique for three out of six datasets.

Many retrieval applications can benefit from multiple modalities, for which how to represent multimodal data is the critical component. Most deep multimodal learning methods typically involve two steps to construct the joint representations: 1) learning of multiple intermediate features, with each intermediate feature corresponding to a modality, using separate and independent deep models; 2) merging the intermediate features into a joint representation using a fusion strategy. However, in the first step, these intermediate features do not have previous knowledge of each other and cannot fully exploit the information contained in the other modalities. In this paper, we present a modal-aware operation as a generic building block to capture the non-linear dependencies among the heterogeneous intermediate features, which can learn the underlying correlation structures in other multimodal data as soon as possible. The modal-aware operation consists of a kernel network and an attention network. The kernel network is utilized to learn the non-linear relationships with other modalities. The attention network finds the informative regions of these modal-aware features that are favorable for retrieval. We verify the proposed modal-aware feature learning in the multimodal hashing task. The experiments conducted on three public benchmark datasets demonstrate significant improvements in the performance of our method relative to state-of-the-art methods.

Performance of neural networks greatly depends on quality, size and balance of training dataset. In a real environment datasets are rarely balanced and training deep models over such data is one of the main challenges of deep learning. In order to reduce this problem, methods and techniques are borrowed from the traditional machine learning. Conversely, generative adversarial networks (GAN) were created and developed, a relatively new type of generative models that are based on game theory and consist of two neural networks, a generator and a discriminator. The generator’s task is to create a sample from the input noise that is based on training data distribution and the discriminator should detect those samples as fake. This process goes through a finite number of iterations until the generator successfully fools the discriminator. When this occurs, sample becomes a part of new (augmented) dataset. Even though the original GAN creates unlabeled samples, variants that soon appeared removed that limitation. Generating artificial data through these networks appears to be a meaningful solution to the imbalance problem since it turned out that artificial samples created by GAN are difficult to differentiate from the real ones. In this manner, new samples of minority class could be created and dataset imbalance ratio lowered.

Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms.

Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations to enable successful domain adaptation. In this paper, a novel method called learning TransFerable and Discriminative Features for unsupervised domain adaptation (TFDF) is proposed to optimize these two objectives simultaneously. On the one hand, distribution alignment is performed to reduce domain discrepancy and learn more transferable representations. Instead of adopting Maximum Mean Discrepancy (MMD) which only captures the first-order statistical information to measure distribution discrepancy, we adopt a recently proposed statistic called Maximum Mean and Covariance Discrepancy (MMCD), which can not only capture the first-order statistical information but also capture the second-order statistical information in the reproducing kernel Hilbert space (RKHS). On the other hand, we propose to explore both local discriminative information via manifold regularization and global discriminative information via minimizing the proposed class confusion objective to learn more discriminative features, respectively. We integrate these two objectives into the Structural Risk Minimization (RSM) framework and learn a domain-invariant classifier. Comprehensive experiments are conducted on five real-world datasets and the results verify the effectiveness of the proposed method.

Graph Convolution Networks (GCNs) have attracted significant attention and have become the most popular method for learning graph representations. In recent years, many efforts have focused on integrating GCNs into recommender tasks and have made remarkable progress. At its core is to explicitly capture the high-order connectivities between nodes in the user-item bipartite graph. However, we found some potential drawbacks existed in the traditional GCN-based recommendation models are that the excessive information redundancy yield by the nonlinear graph convolution operation reduces the expressiveness of the resultant embeddings, and the important popularity features that are effective in sparse recommendation scenarios are not encoded in the embedding generation process.

In this work, we develop a novel GCN-based recommendation model, named Refined Graph convolution Collaborative Filtering (RGCF), where a refined graph convolution structure is designed to match non-semantic ID inputs. In addition, a new fine-tuned symmetric normalization is proposed to mine node popularity characteristics and further incorporate the popularity features into the embedding learning process. Extensive experiments were conducted on three public million-size datasets, and the RGCF improved by an average of 13.45% over the state-of-the-art baseline. Further comparative experiments validated the effectiveness and rationality of each part of our proposed RGCF. We released our code at https://github.com/hfutmars/RGCF.

Recommender systems apply machine learning and data mining techniques for filtering unseen information, and they can provide an opportunity to predict whether a user would be interested in a given item. The main types of recommender systems are collaborative filtering (CF) and content-based filtering, which suffer from scalability and data sparsity resulting in poor quality recommendations and reduced coverage. There are two incremental algorithms based on Singular Value Decomposition (SVD) with high scalability for recommender systems which are named the incremental SVD algorithm and incremental Approximating the Singular Value Decomposition (ApproSVD) algorithm. In both mentioned methods, the estimated value of rank for approximating the recommender systems’ data matrix is chosen experimentally in the related literature. In this paper, we investigate the role of singular values for estimating a more reliable amount of rank in the mentioned dimensionality reduction techniques to improve the recommender systems’ performance. In other words, we offered a strategy for choosing the optimal rank that approximates the data matrix more accurately in incremental algorithms with the help of singular values. The numerical results illustrate that the suggested strategy improves the accuracy of the recommendations and run times of both algorithms when employs for Movielens, Netflix, and Jester dataset.

This paper focuses on the influence maximization problem in social networks, which aims to find some influence nodes that maximize the spread of information. Most existing achievements usually adopt a uniform propagation probability, without considering the topic information. Moreover, the classic Independent Cascade Model and its approximations have suffered from much running time. To overcome this limitation, this paper proposed a Topic based Shortest Path Set algorithm (TSPS). Additionally, a comprehensive set of experiments are conducted on large real-world networks, showing that our proposal provides more impressive results in the aspects of influence spread and running time.

It is important to make sense of the data within its context to propose a useful model to solve a problem. This domain knowledge includes information not contained in the data, but that will help us understand the data to be fed into a machine-learning algorithm and guide us on what features might help our model. Nevertheless, domain knowledge may become insufficient as the input variables increase, forcing the need to try automated feature selection techniques. In this study, we investigate whether the joint use of 1) feature selection techniques, such as Chi-square, Tree-based Feature Selection, Pearson’s Correlation, LASSO, Low Variance, and Recursive Feature Elimination, 2) outlier detection methods such as Isolation-Forest, and 3) Cross-Validation techniques lead to improving the accuracy in multiclass classification in machine learning. Specifically, we address the classification of patterns representing the activation state of cell signaling components into classes that symbolize the different cellular processes triggered in cancer cells. The results presented in this work have shown an accuracy increase with up to 80% fewer input features by only using 3 out of the 16 original descriptors.

Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.

The similar case matching task aims to detect which two cases are more similar for a given triplet. It plays a significant role in the legal industry and thus has gained much attention. Due to the rapid development of natural language processing technology, various deep learning techniques have been applied to similar case matching task and obtained attractive performance. Most existing researches usually focus on encoding legal documents into a continuous vector. However, a unified vector is difficult to model multiple elements of the case. In the real world, cases contain numerous elements, which are the basis for legal practitioners to judge the similarity among cases. Legal experts usually focus on whether the two cases have similar legal elements. It makes this task especially challenging. In this paper, we propose a novel model, namely Interactive Attention Capsule Network (dubbed as IACN). It attempts to simulate the process of judgment by legal experts, which captures fine-grained elements similarity to make an interpretable judgment. In other words, the IACN judges the similarity of the case pairs based on the legal elements. The more similar legal elements of a case pair, the higher the degree of similarity of the case pair. In addition, we devise an interactive dynamic routing mechanism, which can better learn the interactive representation of legal elements among cases than the vanilla dynamic routing. We conduct extensive experiments based on a real-world dataset. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.

As an important task in business process management, remaining time prediction for business process instances has attracted extensive attentions. However, most of the traditional remaining time prediction approaches only take into account formal process models and cannot handle large-scale event logs in an effective manner. Although machine learning and deep learning have been recently applied to the remaining time prediction task, these approaches cannot incorporate domain knowledge naturally. To overcome these weaknesses of existing studies, we propose a remaining execution time prediction approach based on a novel auto-encoded transition system, which can enhance the complementarity of process modeling and deep learning techniques. Through auto-encoding the event-level and state-level features, the proposed approach can represent process instances in a comprehensive and compact form. Furthermore, a transfer learning strategy is proposed to train the remaining time prediction model so as to avoid overfitting and improve the accuracy of prediction. We conduct extensive experiments on four real-world datasets to verify the effectiveness of the proposed approach. The results show its superiority over several state-of-the-art approaches.