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Label-specific features learning is a multi-label learning framework that utilizes label feature extraction to solve a single example where multiple class labels exist simultaneously. As an essential multi-label learning method, label correlation learning has been widely used in multi-label classification learning. However, in the existing label-specific features learning, the label correlation measurement often assumes that the label correlations are a global structure or that the label correlations only have a local smoothness. In actual application scenarios, the two situations may occur together. This paper proposes a multi-label classification method by joint Label-Specific features and Global and Local label correlation learning, named LSGL. Firstly, we obtain the weight of the label-specific features of each class label utilizing the l 1 -norm and then learn high-order global label correlation and label local smoothness. By adding manifold regularization terms, we fully utilize the structural relationship between features and labels and mine global and local label association information. These processes are carried out in a unified optimization model, and each part learns and promotes each other. Finally, in the low-dimensional label-specific features representation learning is to carry out multi-label classification learning through the support vector machine and the extreme learning machine, respectively. A comparative study with state-of-the-art approaches and statistical hypothesis testing manifests the validity of the LSGL method and the features learned from label-specific features learning. [ABSTRACT FROM AUTHOR]

Learning from categorical data is a critical yet challenging task. Current research focuses on either leveraging the complex interaction between and within categorical values to generate a numerical representation, or designing a model that can tackle this types of data directly. However, both of these paradigms overlook the relation between the data characteristics and learning model hypothesis. In this paper, we propose a model-aware categorical data embedding framework that jointly reveals the intrinsic categorical data characteristics and optimizes the fitness of the representation for the follow-up learning model. An ELM-aware and a SVM-aware representation methods have been instantiated under this framework. Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Stock price volatility prediction is regarded as one of the most attractive and meaningful research issues in financial market. Some existing researches have pointed out that both the prediction accuracy and the prediction speed are the most important factors in the process of stock prediction. In this paper, we focus on the problem of how to design a methodology which can improve prediction accuracy as well as speed up prediction process, and propose a new prediction model which employs mutual information- based sentimental analysis methodology with extreme learning machine to enhance the prediction performance. The two major contributions of our work are (1) as the words in the news documents are not absolutely negative or positive, and the lengths of the financial news documents are various; here, we propose a new sentimental analysis methodology based on mutual information to improve the efficiency of feature selection, which is different from the traditional sentimental analysis algorithm, and a new weighting scheme is also used in the feature weighting process; (2) since ELM is a fast learning model and has been successfully applied in many research fields, we propose a prediction model which combined mutual information-based sentimental analysis with kernel-based ELM named as MISA-K-ELM. This model has the benefits of both statistical sentimental analysis and ELM, which can well balance the requirements of both prediction accuracy and prediction speed. We take experiments on HKEx 2001 stock market datasets to validate the performance of the proposed MISA-K-ELM. The market historical price and the market news are implemented in our MISA-K-ELM. To test the efficiency of MISA, we first compare the prediction accuracy of ELM model using MISA with ELM model using traditional sentimental analysis. Then, we compare our proposed MISA-K-ELM with existing state-of-the-art learning algorithms, such as Back-Propagation Neural Network (BP-NN), and Support Vector Machine (SVM). Our experimental results show that (1) MISA model can help get higher prediction accuracy than traditional sentimental analysis models; (2) MISA-K-ELM and MISA-SVM have a higher prediction accuracy than MISA-BP-NN and MISA-B-ELM; (3) both MISA-K-ELM and MISA-B-ELM can achieve faster prediction speed than MISA-SVM and MISA-BP-NN in most cases; (4) in most cases, MISA-K-ELM has higher prediction accuracy than the other three methodologies. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM), as an emergent technique for training feed-forward neural networks, has shown good performances on various learning domains. This paper investigates the impact of random weights during the training of ELM. It focuses on the randomness of weights between input and hidden layers, and the dimension change from input layer to hidden layer. The direct motivation is to verify as to whether during the training of ELM, the randomly assigned weights exert some positive effects. Experimentally we show that for many classification and regression problems, the dimension increase caused by random weights in ELM has a performance better than the dimension increase caused by some kernel mappings. We assume that via the random transformation, output-samples are more concentrate than input-samples which will make the learning more efficient. [ABSTRACT FROM AUTHOR]