High Number Of Features Research Articles

Acoustic feature selection for automatic emotion recognition from speech

Emotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any language or linguistic information remains an unclosed problem. The limited size of existing emotional data samples, and the relative higher dimensionality have outstripped many dimensionality reduction and feature selection algorithms. This paper focuses on the data preprocessing techniques which aim to extract the most effective acoustic features to improve the performance of the emotion recognition. A novel algorithm is presented in this paper, which can be applied on a small sized data set with a high number of features. The presented algorithm integrates the advantages from a decision tree method and the random forest ensemble. Experiment results on a series of Chinese emotional speech data sets indicate that the presented algorithm can achieve improved results on emotional recognition, and outperform the commonly used Principle Component Analysis (PCA)/Multi-Dimensional Scaling (MDS) methods, and the more recently developed ISOMap dimensionality reduction method.

Théorie du consensus appliquée au prétraitement des ensembles de données

Visualization methods do not scale well with high number of features. We present an approach using a consensus theory based feature selection (CTBFS) algorithm, clustering for sampling and visualization for weight assignment in order to aggregate multivariate and multidimensional datasets. We use datasets available in UCI and Kent Ridge Bio Medical Dataset Repositories in order to evaluate the performance of our new approach.

An approach towards method development for untargeted urinary metabolite profiling in metabonomic research using UPLC/QToF MS

The application of LC–MS for untargeted urinary metabolite profiling in metabonomic research has gained much interest in recent years. However, the effects of varying sample pre-treatments and LC conditions on generic metabolite profiling have not been studied. We aimed to evaluate the effects of varying experimental conditions on data acquisition in untargeted urinary metabolite profiling using UPLC/QToF MS. In-house QC sample clustering was used to monitor the performance of the analytical platform. In terms of sample pre-treatment, results showed that untreated filtered urine yielded the highest number of features but dilution with methanol provided a more homogenous urinary metabolic profile with less variation in number of features and feature intensities. An increased cycle time with a lower flow rate (400 μl/min vs 600 μl/min) also resulted in a higher number of features with less variability. The step elution gradient yielded the highest number of features and the best chromatographic resolution among three different elution gradients tested. The maximum retention time and mass shift were only 0.03 min and 0.0015 Da respectively over 600 injections. The analytical platform also showed excellent robustness as evident by tight QC sample clustering. To conclude, we have investigated LC conditions by studying variability and repeatability of LC–MS data for untargeted urinary metabolite profiling.

The impact of feedback semantics in visual word recognition: number-of-features effects in lexical decision and naming tasks.

The notion of feedback activation from semantics to both orthography and phonology has recently been used to explain a number of semantic effects in visual word recognition, including polysemy effects (Hino & Lupker, 1996; Pexman & Lupker, 1999) and synonym effects (Pecher, 2001). In the present research, we tested an account based on feedback activation by investigating a new semantic variable: number of features (NOF). Words with high NOF (e.g., LION) should activate richer semantic representations than do words with low NOF (e.g., LIME). As a result, the feedback activation from semantics to orthographic and phonological representations should be greater for high-NOF words, which should produce superior lexical decision task (LDT) and naming task performance. The predicted facilitory NOF effects were observed in both LDT and naming.

Feature selection on hierarchy of web documents

The paper describes feature subset selection used in learning on text data (text learning) and gives a brief overview of feature subset selection commonly used in machine learning. Several known and some new feature scoring measures appropriate for feature subset selection on large text data are described and related to each other. Experimental comparison of the described measures is given on real-world data collected from the Web. Machine learning techniques are used on data collected from Yahoo, a large text hierarchy of Web documents. Our approach includes some original ideas for handling large number of features, categories and documents. The high number of features is reduced by feature subset selection and additionally by using ‘stop-list’, pruning low-frequency features and using a short description of each document given in the hierarchy instead of using the document itself. Documents are represented as feature-vectors that include word sequences instead of including only single words as commonly used when learning on text data. An efficient approach to generating word sequences is proposed. Based on the hierarchical structure, we propose a way of dividing the problem into subproblems, each representing one of the categories included in the Yahoo hierarchy. In our learning experiments, for each of the subproblems, naive Bayesian classifier was used on text data. The result of learning is a set of independent classifiers, each used to predict probability that a new example is a member of the corresponding category. Experimental evaluation on real-world data shows that the proposed approach gives good results. The best performance was achieved by the feature selection based on a feature scoring measure known from information retrieval called Odds ratio and using relatively small number of features.