Advances in computational intelligence最新文献

This paper proposes some distributed algorithms to solve the sparse factorization of a large-scale nonnegative matrix (SFNM). These distributed algorithms combine some merits of classical nonnegative matrix factorization (NMF) algorithms and distributed learning network. Our proposed algorithms utilize the whole nodes of network to solve a factorization problem of a nonnegative matrix; the fact is that per node copes with a part of the matrix, then uses the distributed average consensus (DAC) algorithm or regional nodes to communicate the parameters gained by each node to ensure them to be convergent or easy to calculation. Different from other existing distributed learning algorithms of NMF, which always need high-qualified hardware or complicated computing methods, our algorithms make a full use of the simplicity of traditional NMF algorithms and distributed thoughts. Some artificial datasets are used for testing these algorithms, and the experimental results with comparisons show that the proposed algorithms perform favorably in terms of accuracy and efficiency.

In reality, like single-label data, multi-label data sets have the problem that only some have labels. This is an excellent challenge for multi-label feature selection. This paper combines the logistic regression model with graph regularization and sparse regularization to form a joint framework (SMLFS) for semi-supervised multi-label feature selection. First of all, the regularization of the feature graph is used to explore the geometry structure of the feature, to obtain a better regression coefficient matrix, which reflects the importance of the feature. Second, the label graph regularization is used to extract the available label information, and constrain the regression coefficient matrix, so that the regression coefficient matrix can better fit the label information. Third, the (L_{2,p})-norm (0<ple 1) constraint is used to ensure the sparsity of the regression coefficient matrix so that it is more convenient to distinguish the importance of features. In addition, an iterative updating algorithm with convergence is designed and proved to solve the above problems. Finally, the proposed method is validated on eight classic multi-label data sets, and the experimental results show the effectiveness of the proposed algorithm.

Open information extraction (Open IE) is a core task of natural language processing (NLP). Even many efforts have been made in this area, and there are still many problems that need to be tackled. Conventional Open IE approaches use a set of handcrafted patterns to extract relational tuples from the corpus. Secondly, many NLP tools are employed in their procedure; therefore, they face error propagation. To address these problems and inspired by the recent success of Generative Adversarial Networks (GANs), we employ an adversarial training architecture and name it Adversarial-OIE. In Adversarial-OIE, the training of the Open IE model is assisted by a discriminator, which is a (Convolutional Neural Network) CNN model. The goal of the discriminator is to differentiate the extraction result generated by the Open IE model from the training data. The goal of the Open IE model is to produce high-quality triples to cheat the discriminator. A policy gradient method is leveraged to co-train the Open IE model and the discriminator. In particular, due to insufficient training, the discriminator usually leads to the instability of GAN training. We use the distant supervision method to generate training data for the Adversarial-OIE model to solve this problem. To demonstrate our approach, an empirical study on two large benchmark dataset shows that our approach significantly outperforms many existing baselines.

Maintaining of immense measure of data has always been a great concern. With expansion in awareness towards educational data, the amount of data in the educational institutes is additionally expanded. To deal with increasing growth of data leads to the usage of a new approach of machine learning. Predicting student’s performance before the final examination can help management, faculty, as well as students to make timely decisions and avoid failing of students. In addition to this, the usage of sentimental analysis can gain insight to improve their performance on the student’s next term. We have used various machine learning techniques such as XGboost, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) to build predictive models. We have evaluated the performance of these techniques in terms of the performance indicators such as accuracy, precision and recall to determine the better technique that gives accurate results. The evaluation shows that XGBoost is superior in the prediction of poor academic performers than SVM and K-NN with large dataset.

With many physical processes in which quantum mechanical phenomena can occur, it is essential to take into account a decision mechanism based on measurement data. This can be achieved by means of so-called numerical events, which are specified as follows: Let S be a set of states of a physical system and p(s) the probability of the occurrence of an event when the system is in state (sin S). A function (p:Srightarrow [0,1]) is called a numerical event or alternatively, an S-probability. If a set P of S-probabilities is ordered by the order of real functions, it becomes a poset which can be considered as a quantum logic. In case the logic P is a Boolean algebra, this will indicate that the underlying physical system is a classical one. The goal of this paper is to study sets of S-probabilities which are not far from being Boolean algebras by means of the addition and comparison of functions that occur in these sets. In particular, certain classes of so-called Boolean posets of S-probabilities are characterized and related to each other and descriptions based on sets of states are derived.

In this contribution the solution of Fractional Optimal Control Problems (FOCP) by using the Orthogonal Collocation Method (OCM) and the Multi-objective Optimization Stochastic Fractal Search (MOSFS) algorithm is investigated. For this purpose, three classical case studies on engineering are considered. Initially, the concentration profiles of laccase enzyme production process are analyzed to evaluate the influence of fractional order. Then, two classical FOCP (Catalyst Mixing and Batch Reactor) are solved by using the association between OCM and MOSFS approachesthrough the formulation and solution of a multi-objective optimization problem. The results indicate that the variation of the fractional order impliesdifferent values for the original objective function. In addition, physicallyincoherent profiles can be obtained by considering the fluctuation of the fractional order. Finally, the proposed MOSFS is considered as apromising methodology to solve multi-objective optimization problems.

Collaborator recommendation aims to seek suitable collaborators for a given author. In this paper, we model all authors and their features as an attributed graph, and then perform community search on the attributed graph to locate the best collaborator community. From the early collaborative filtering-based methods to the recent deep learning-based methods, most existing works usually unilaterally weigh the network structure or node attributes, or directly search the community via the given node. We argue that the inherent disadvantage of these methods is that the quality of the node to be recommended may not be high, which can lead to suboptimal recommendation results.

In this work, we develop a new recommendation framework, i.e., Collaborator Recommendation Integrating Author’s Cooperation Strength and Research Interests (CRISI) on an attributed graph. It improves the quality of recommended node via double-weighting the structure and attributes as well as adopting the node replacement method. This can effectively recommend collaborators who have a close cooperative relationship with the recommended node. We conduct extensive experiments on two real-world datasets, and further analysis shows that the performance of our proposed CRISI model is superior to existing methods.

Commonly used similarity-based algorithms in memory-based collaborative filtering may provide unreliable and misleading results. In a cold start situation, users may find the most similar neighbors by relying on an insufficient number of ratings, resulting in low-quality recommendations. Such poor recommendations can also result from similarity metrics as they are incapable of capturing similarities among uncommon items. For example, when identical items between two users are popular items, and both users rated them with high scores, their different preferences toward other items are hidden from similarity metrics. In this paper, we propose a method that estimates the final ratings based on a combination of multiple ratings supplied by various similarity measures. Our experiments show that this combination benefits from the diversity within similarities and offers high-quality personalized suggestions to the target user.