This paper describes design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter ap...

The goal of this paper is to propose an ensemble classification method for the credit assignment problem. The idea of the proposed method is based on switching class labels techniques. An application of such techniques allows solving two typical data mining problems: a predicament of imbalanced dataset, and an issue of asymmetric cost matrix. The performance of the proposed solution is evaluate...

This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis mo...

A method for tuning MLP learning parameters in an ensemble classifier framework is presented. No validation set or cross-validation technique is required to optimize parameters for generalisability. In this paper, the technique is applied to face recognition using Error-Correcting Output Coding strategy to solve multiclass problems.

In this paper, we present a combination of different types of sentiment analysis approaches in order to improve the individual performance of them. These ones consist of (I) ranking algorithms for scoring sentiment features as bi-grams and skip-grams extracted from annotated corpora; (II) a polarity classifier based on a deep learning algorithm; and (III) a semi-supervised system founded on the...

Microarray gene expression technology provides a systematic approach to patient classification. However, microarray data pose a great computational challenge owing to their large dimensionality, small sample sizes, and potential correlations among genes. A recent study has shown that gene-gene correlations have a positive effect on the accuracy of classification models, in contrast to some prev...

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed sol...

Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party’s private data? We propose to transfer the ‘knowledge’ of the local classifier ensemble by first c...

This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The ...

How to combine the outputs from base classifiers is a key issue in ensemble learning. This paper presents a dynamic classifier ensemble method termed as DCE-CC. It dynamically selects a subset of classifiers for test samples according to classification confidence. The weights of base classifiers are learned by optimization of margin distribution on the training set, and the ordered aggregation ...