Abstract — This paper explores a novel ensemble technique for unsupervised classification using nonparametric statistics. Multiple classification systems (MCS), or ensemble techniques, involve considering several classification methods or multiple outputs from the same method and devising techniques to reach a decision. The performance of a binary classification system can be measured on a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) is exactly the Wilcoxon Rank Sum or Mann-Whitney U statistic, both of which are nonparametric statistics based upon ranked data. Successful performance of an unsupervised ensemble can be measured through the AUC, and the performance of different aggregation techniques for the combination of the multiple classification system decision values, or rankings in this paper, is illustrated. Aggregation techniques are based upon fuzzy logic theory, creating the fuzzy ROC curve. The one-class SVM is utilized for the unsupervised classification. I.

Abstract — This paper proposes a novel method of fusing models for classification of unbalanced data. The unbalanced data contains a majority of healthy (negative) instances, and a minority of unhealthy (positive) instances. The applicability of this type of classification problem with security applications inspired the naming of such problems as security classification problems (SCP). The area under the ROC curve (AUC) is the metric utilized to measure classifier performance, and in order to better understand AUC and ROC behavior, pseudo-ROC curves created from simulated data are introduced. ROC curves depend entirely upon the rankings created by classifiers. The rank distributions discussed in this paper display classifier performance in a novel form, and the behavior of these rank distributions provides insight into classifier fusion for the SCP. Rank distributions, which illustrate the probability of a particular rank containing a positive or negative instance, will be introduced and used to explain why synergistic classifier fusion occurs. I.

Abstract. Acoustic events produced in meeting-room-like environments may carry information useful for perceptually aware interfaces. In this paper, we focus on the problem of combining different information sources at different structural levels for classifying human vocal-tract non-speech sounds. The Fuzzy Integral (FI) approach is used to fuse outputs of several classification systems, and feature selection and ranking are carried out based on the knowledge extracted from the Fuzzy Measure (FM). In the experiments with a limited set of training data, the FI-based decision-level fusion showed a classification performance which is much higher than the one from the best single classifier and can surpass the performance resulting from the integration at the featurelevel by Support Vector Machines. Although only fusion of audio information sources is considered in this work, the conclusions may be extensible to the multi-modal case. 1

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Abstract — A multi-classifier system- obtained by combining several individual classifiers- usually exhibits a better performance (precision) than any of the original classifiers. In this work we use a multi-classifier based on a forest of randomly generated fuzzy decision trees (Fuzzy Random Forest), and we propose a new method to combine their decisions to obtain the final decision of the forest. The proposed combination is a weighted method based on the concept of local fusion and on the data set Out Of Bag (OOB) error.

Abstract—A large experiment on Persian handwritten digits are reported and discussed. In this paper the techniques to combine multiple classifiers based on static structures is investigated. A static structure includes two main strategies to combine result of base classifiers: a) class indifferent methods b) class conscious methods. We establish our model on Decision Template and Dempster Shafer, which are under category of class indifferent method, and compare theirs recognition rate with five of the most famous combining methods of class conscious category. To evaluate our proposed model a real-world database of Persian handwritten digits containing 8600 handwritten digit images is used. Experiments using our database demonstrate that combining result of base classifiers with class indifferent methods indeed are far more effective than combining the result with class conscious methods in Persian handwritten digit recognition. Evaluating the proposed system with 2150 test samples the recognition rate of 91.98 % is achieved. Keywords—Class conscious, Class indifferent, Classifier fusion,