Microarray experiments on gene expression inevitably generate missing values, which impedes further downstream biological analysis. Therefore, it is key to estimate the missing values accurately. Most of the existing imputation methods tend to suffer from the over-fitting problem. In this study, we propose two regularized local learning methods for microarray missing value imputation. Motivated by the grouping effect of $L_{2}$L2 regularization, after selecting the target gene, we train an $L_{2}$L2 Regularized Local Least Squares imputation model (RLLSimpute_L2) on the target gene and its neighbors to estimate the missing values of the target gene. Furthermore, RLLSimpute_L2 imputes the missing values in an ascending order based on the associated missing rate with each target gene. This contributes to fully utilizing the previously estimated values. Besides $L_{2}$L2, we further explore $L_{1}$L1 regularization and propose an $L_{1}$L1 Regularized Local Least Squares imputation model (RLLSimpute_L1). To evaluate their effectiveness, we conducted extensive experimental studies on six benchmark datasets covering both time series and non-time series cases. Nine state-of-the-art imputation methods are compared with RLLSimpute_L2 and RLLSimpute_L1 in terms of three performance metrics. The comparative experimental results indicate that RLLSimpute_L2 outperforms its competitors by achieving smaller imputation errors and better structure preservation of differentially expressed genes.

Feature selection is a significant aspect of speech emotion recognition system. How to select a small subset out of the thousands of speech data is important for accurate classification of speech emotion. In this paper we investigate heuristic algorithm Harmony search (HS) for feature selection. We extract 3 feature sets, including MFCC, Fourier Parameters (FP), and features extracted with The Munich open Speech and Music Interpretation by Large Space Extraction (openSMILE) toolkit, from Berlin German emotion database (EMODB) and Chinese Elderly emotion database (EESDB). And combine MFCC with FP as the fourth feature set. We use Harmony search to select subsets and decrease the dimension space, and employ 10-fold cross validation in LIBSVM to evaluate the change of accuracy between selected subsets and original sets. Experimental results show that each subset's size reduced by about 50%, however, there is no sharp degeneration on accuracy and the accuracy almost maintains the original ones.

Recently, studies have been performed on harmony features for speech emotion recognition. It is found in our study that the first- and second-order differences of harmony features also play an important role in speech emotion recognition. Therefore, we propose a new Fourier parameter model using the perceptual content of voice quality and the first- and second-order differences for speaker-independent speech emotion recognition. Experimental results show that the proposed Fourier parameter (FP) features are effective in identifying various emotional states in speech signals. They improve the recognition rates over the methods using Mel frequency cepstral coefficient (MFCC) features by 16.2, 6.8 and 16.6 points on the German database (EMODB), Chinese language database (CASIA) and Chinese elderly emotion database (EESDB). In particular, when combining FP with MFCC, the recognition rates can be further improved on the aforementioned databases by 17.5, 10 and 10.5 points, respectively.

Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor (KNN) classifier embedded. Instead of taking KNN as a black box, we incrementally construct and maintain a classifier distance matrix to speed up the gene selection process. Experiments on six publicly available microarrays were first conducted to show the effectiveness of incremental wrapper based gene selection method with KNN. Then, to demonstrate the performance gain in time cost reduction, we analyzed the time complexity and experimentally evaluated it. Both theoretical analysis and experimental results prove that the proposed approach greatly accelerates the gene selection process without degrading the classification accuracy.

Gene selection plays a crucial role in the analysis of microarray data with high dimensionality and small sample size. Incremental wrapper based feature subset selection (FSS) methods, among various feature selection approaches, tend to obtain high quality feature subset and better classification accuracy than filter methods, while it is much more time consuming since the interdependence and redundancy between features is evaluated in a wrapper way. In this paper, we explore to introduce Markov Blanket (MB) into incremental wrapper based FSS process. Rather than evaluate the quality of all the features ranked by a filter method, our proposal eliminates features that are redundant to the newly selected one via MB during the wrapper evaluation process to reduce the number of wrappers, enabling us to select the relevant features and eliminate redundant ones efficiently. To verify the effectiveness and efficiency of the proposed approach, experimental comparisons on six publicly available microarray data are conducted with two typical classifiers with different metrics, Naïve Bayes and 1-Nearest-Neighbor. Experimental results demonstrate that our approach greatly speeds up the feature selection process, obtains more compact feature subset and achieves better classification accuracy compared to that without MB for both two-category and multi-category problems.

Correctly discovering causal relations from healthcare information can help people to understand disease mechanisms and discover disease causes. In some cases, the healthcare data do not follow a multivariate Gaussian distribution. We design a new causal structure learning algorithm. The algorithm can effectively combines ideas from local learning with simultaneous equations models techniques. In the first phase of the algorithm we select potential neighbors for each variable based on simultaneous equations models, and then perform a constrained hill-climbing search to orient the edges. Using the algorithm without prior knowledge, we analyze causal relations in the real data from the National Health and Nutrition Examination Survey.

Finding information about people using search engines is one of the most common activities on the Web. However, search engines usually return a long list of Web pages, which may be relevant to many namesakes, especially given the explosive growth of Web data. To address the challenge caused by name ambiguity in Web people search, this paper proposes a novel graph-based framework, GRAPE (abbr. a Graph-based fRamework for disAmbiguating People appEarances in Web search). In GRAPE, people tag information (e.g., people name, organization, and email address) surrounding the queried people name is extracted from the search results, a graph-based unsupervised algorithm is then developed to cluster the extracted tags, where a new method, Cohesion, is introduced to measure the importance of a tag for clustering, and each final cluster of tags represents a unique people entity. Experimental results show that our proposed framework outperforms the state-of-the-art Web people name disambiguation approaches.

The ever growing volume of Web data makes it increasingly challenging to accurately find relevant information about a specific person on the Web. To address the challenge caused by name ambiguity in Web people search, this paper explores a novel graph-based framework to both disambiguate and tag people entities in Web search results. Experimental results demonstrate the effectiveness of the proposed framework in tag discovery and name disambiguation.