Selecting the optimal number of features in a classifier ensemble normally requires a validation set or cross-validation techniques. In this paper, feature ranking is combined with Recursive Feature Elimination (RFE), which is an effective technique for eliminating irrelevant features when the feature dimension is large. Stopping criteria are based on out-of-bootstrap (OOB) estimate and class s...

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples the multi-class classification problem. To build ECNN, we propose design code matrix so minimu...

A major concern with supervised learning techniques for text classification is that they often require a large number of labeled examples to learn accurately. One way to reduce the amount of labeled data required is to develop algorithms that can learn effectively from a small number of labeled examples augmented with a large number of unlabeled examples. In this paper, we develop a framework t...

Previous research has shown that a technique called error correcting output coding ECOC can dramatically improve the classi cation accuracy of supervised learning algorithms that learn to classify data points into one of k classes This paper presents an empirical investigation of why the ECOC technique works particularly when employed with decision tree learning methods It concludes that an imp...

A distance-based conditional model on the ranking poset is presented for use in classification and ranking. The model is an extension of the Mallows model, and generalizes the classifier combination methods used by several ensemble learning algorithms, including error correcting output codes, discrete AdaBoost, logistic regression and cranking. The algebraic structure of the ranking poset leads...

The classification of 242 measurements in 14 classes is attempted using two different classification approaches. Measurements have been performed with a commercial Electronic Nose comprising 11 chemical sensors on extra-virgin olive oils of 14 different geographical provenances. As we deal with a relatively small data set and a big number of classes, the classification task is quite demanding. ...

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large numbe...

This paper generalizes parallel error correcting codes proposed by Ahlswede et al. over a new type of multiple access channel called parallel error channel. The generalized parallel error correcting codes can handle with more errors compared with the original ones. We show construction methods of independent and non-independent parallel error correcting codes and decoding methods. We derive som...