Abstract—The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. This paper explores the detection of domain-specific emotions using language and discourse information in conjunction with acoustic correlates of emotion in speech signals. The specific focus is on a case study of detecting negative and non-negative emotions using spoken language data obtained from a call center application. Most previous studies in emotion recognition have used only the acoustic information contained in speech. In this paper, a combination of three sources of information—acoustic, lexical, and discourse—is used for emotion recognition. To capture emotion information at the language level, an information-theoretic notion of emotional salience is introduced. Optimization of the acoustic correlates of emotion with respect to classification error was accomplished by investigating different feature sets obtained from feature selection, followed by principal component analysis. Experimental results on our call center data show that the best results are obtained when acoustic and language information are combined. Results show that combining all the information, rather than using only acoustic information, improves emotion classification by 40.7 % for males and 36.4 % for females (linear discriminant classifier used for acoustic information). Index Terms—Acoustic correlates, dialog systems, emotion recognition, emotional salience, feature selection, information fusion, principal component analysis, spoken language processing. I.

When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost always suboptimal.

Independence between individual classifiers is typically viewed as an asset in classifier fusion. We study the limits on the majority vote accuracy when combining dependent classifiers. Q statistics are used

Bias-variance analysis provides a tool to study learning algorithms and can be used to properly design ensemble methods well tuned to the properties of a specific base learner. Indeed the effectiveness of ensemble methods critically depends on accuracy, diversity and learning characteristics of base learners. We present an extended experimental analysis of bias-variance decomposition of the error in Support Vector Machines (SVMs), considering Gaussian, polynomial and dot product kernels. A characterization of the error decomposition is provided, by means of the analysis of the relationships between bias, variance, kernel type and its parameters, offering insights into the way SVMs learn. The results show that the expected trade-off between bias and variance is sometimes observed, but more complex relationships can be detected, especially in Gaussian and polynomial kernels. We show that the bias-variance decomposition offers a rationale to develop ensemble methods using SVMs as base learners, and we outline two directions for developing SVM ensembles, exploiting the SVM bias characteristics and the bias-variance dependence on the kernel parameters. Keywords: Bias-variance analysis, support vector machines, ensemble methods, multi-classifier systems.

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools have been currently developed. Intrusion Detection Systems aim at detecting intruders who elude “first line ” protection. In this paper, a pattern recognition approach to network intrusion detection based on the fusion of multiple classifiers is proposed. Five decision fusion methods are as-sessed by experiments and their performances compared. The potentialities of classifier fu-sion for the development of effective intrusion detection systems are evaluated and discussed.

So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is clearly beyond the state of the art. A clear theoretical comparison is lacking even if one focuses on specific classes of combiners (e.g., linear combiners). In this paper, we theoretically compare simple and weighted averaging rules for fusion of imbalanced classifiers.

Abstract not found

Combining di#erent algorithms submitted to the Third International Fingerprint Verification Competition (FVC2004) is studied.

Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran 2-fold cross-validation experiments on 6 benchmark data sets to compare the fuzzy and non-fuzzy combination methods. On the "fuzzy side" we used the fuzzy integral and the decision templates with different similarity measures. On the "non-fuzzy side" we tried simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive Bayes combination. Surprisingly, the minimum, maximum, average and product, which have been reported elsewhere to work very well on a variety of problems, appeared to be inadequate for our task. Thus the real contest was among the fuzzy combination methods on the one hand, and the weighted majority vote, the simple majority vote, and the Naive Bayes combiner, on the other hand. In our experiments, the fuzzy methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners. The majority vote and the Naive Bayes combiners had erratic behavior, ranging from the best to the worst contestants for different data sets.

Abstract ∗ Classifier combination is now an established pattern recognition subdiscipline. Despite the strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and empirical solutions. Assuming that “soft computing ” encompasses neural networks, evolutionary computation, and fuzzy sets, we explain how each of the three components has been used in classifier combination. Let D = {D1, D2,..., DL} be a set of classifiers (we shall also call D a team or ensemble), and let Ω = {ω1,..., ωc} be a set of class labels. Each classifier gets as its input a feature vector x = [x1,..., xn] T, x ∈ ℜ n and assigns it to a class label from Ω, i.e., Di: ℜ n → Ω. Alternatively, we may define the classifier