The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a f...

Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization .Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which we wa...

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitti...

Ensemble learning has been a hot topic in machine learning due to its successful utilization in many applications. Rough set theory has been proved to be an excellent mathematical tool for dimension reduction. In this paper, based on rough set, a novel framework for ensemble is proposed. In our proposed framework, the relationship among attributes in rough subspace is first considered, and the ...

In this chapter, the basics of the rough set approach are presented, and an outline of an exemplary processor structure is given. The organization of a simple processor is based on elementary rough set granules and dependencies between them. The rough set processor (RSP) is meant to be used as an additional fast classification unit in ordinary computers or as an autonomous learning machine. In ...

Attribute Reduction has a significant role in different branches of artificial intelligence like machine learning, pattern recognition, data mining from databases etc. This paper deals with reduction of unimportant attribute(s) for classification and decision making, using Fuzzy-Rough set. A survey of Fuzzy-Rough set based methods for attribute reduction is presented here.

One-class classification problemhas been investigated thoroughly for past decades. Among one of themost effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow...

Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discri...

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen ...

The extreme learning machine (ELM) is a newly emerging supervised learning method. In order to use the information provided by unlabeled samples and improve the performance of the ELM, we deformed the kernel in the ELM by modeling the marginal distribution with the graph Laplacian, which is built with both labeled and unlabeled samples. We further approximated the deformed kernel by means of ra...