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In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as...
The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the...
Gene expression based cancer classification using classifier ensembles is the main focus of this work. A new ensemble method is proposed that combines predictions of a small number of k-nearest neighbor (k-NN) classifiers with majority vote. Diversity of predictions is guaranteed by assigning a separate feature subset, randomly sampled from the original set of features, to each classifier. Accuracy...
The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and neural networks (NN) are explained and the specifications of this method are described as an NN application. The improved version of this system which is related to the Gabor filter was tested and an accuracy...
Supervised ANNs such as Learning Vector Quantization (LVQs) and Multi-Layer Perceptrons (MLPs) usually do not support data visualization beside classification. Unsupervised visualization focused ANNs such as Self-organizing Maps (SOM) and its variants such as Visualization induced SOM (ViSOM) on the other hand, usually do not optimize data classification as compared with supervised ANNs such as LVQ...
Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper,...
Gaussian processes is a very promising novel technology that has been applied for both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification case. The reason is that we encounter intractable integrals. In this paper we propose a new approximate solution for the Gaussian process classification...
Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature election method for support vector machine is proposed. Based on pattern similarity measurement in kernel space, lass...
This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data...
Semantic scene classification, robotic state recognition, and many other real-world applications involve multi-label classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm...
This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that...
Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the...
The paper presents a generalization of the framework for assessment of predictive models uncertainty using machine learning techniques. Historical model errors which are mismatch between observed and predicted values are assumed to be indicators of total model uncertainty; it is measured in the form of prediction intervals, and comprises all sources of uncertainty including model structure, model...
In the context of Ensembles or Multi-Classifier Systems, the choice of the ensemble members is a very complex task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a great deal of research to find effective classifier member selection methods. In this paper, we propose a selection criterion based on both the accuracy and...
In the paper, the multi-class pattern classification using extreme learning machine (ELM) is studied. The study is based on either a series of ELM binary classifiers or a single ELM classifier. When using binary ELM classifiers, the multi-class problem is decomposed into two-class problem using the one-against-all (OAA) and one-against-one (OAO) schemes, which are named as ELM-OAA and ELM-OAO respectively...
In this paper, we address the trajectory classification problem in Gaussian process framework without using Gaussian process based classification directly. Properties of the function corresponding to a trajectory are captured into the hyperparameters of a Gaussian process. As different trajectories have different properties, hyperparameters are different for these trajectories. In the hyperparametric...
We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high-dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic...
In this paper, we propose a new kernel discriminant analysis using composite vectors (C-KDA). We show that employing composite vectors is similar to using more samples by analysis, which is a great advantage in classification problems when the size of training samples is small. Motivated by this, we apply composite vectors to kernel-based methods, which may have overfitting problems when training...
This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure...
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm...
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