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High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
Classification of EEG signals is an important task in Brain Computer Interface (BCI) research. However, the large number of attributes of EEG data is regarded as a curse for classifiers. This paper aims at dimensionality reduction of EEG signals. We use rough set theory to reduce the dimensions of EEG data. In particular, we use discernibility matrix (DM) to compute an indispensable set of attributes...
This paper examines the quality of feature set obtained from Wavelet based Energy-entropy with variation of scale and wavelet type. Here motor imagery of left-right hand movement classification problem has been studied. Elliptic bandpass filters are used to discard unwanted signals and also to extract alpha & beta rhythms. We have implemented wavelet-based energy-entropy with three level of decomposition...
This paper proposes a new technique for simultaneous estimation of missing values using Weighted kNN approach whose weight is computed by the product of Grey relational grade and weight vector of SVM. A mixture kernel is used which is a hybridization of Polynomial and Radial Basis Kernel. Applying this proposed technique to fisher iris dataset, we find that the mixture kernel gives better result....
This paper focuses on the classification of motor imagery of the left-right hand movements from a healthy subject. Elliptic Bandpass filters are used to discard the unwanted signals. Our study was on C3 and C4 electrodes particularly for the left-right limb movements. We deployed various feature extraction techniques on the EEG data. Statistical-based, wavelet-based energy-entropy & RMS,...
This paper proposes a new multi-objective optimization algorithm inspired by the Newtonian Law of Gravity. The new algorithm is a multiple objective extension of the single objective optimization Gravitational Search Algorithm. Generally, we know various multi-objective algorithms with leader selection schemes. The Leader guides the population towards Pareto fronts. As a result, the algorithm becomes...
We investigate the Coulomb breakup of neutron-rich nuclei 11 Be and 19,17,15 C within a theory developed in the framework of Distorted Wave Born Approximation. Finite range effects are included by a local momentum approximation, which allows incorporation of realistic wave functions for these nuclei in our calculations. Energy and angular as...
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