A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the...

Transfer learning is a promising approach for reducing training time in brain-computer interface (BCI). However, how to effectively transfer data from previous users new user poses huge challenge. This paper presents novel that combines alignment and source subject selection motor imagery (MI) based BCIs. The former achieved by reference matrix the regularization of two matrices estimated Riema...

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, inc...

A multiple classifier system to localize stem-ends and calyxes of apple fruits was introduced previously. In this paper we not only introduce a new decision step to this system, but also provide comparisons of several feature selection algorithms and classifiers used. Our results prove that floating forward selection is the best within heuristic methods and support vector machines are better th...

Feature selection is an importance step in classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, redundant, irrelevant data, and optimize the classification performance. This paper addresses a new subset feature selection performed by a new Social Spider Optimizer algorithm (SSOA) to find optimal regions of th...

This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Fi...

Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report a...

Brain Computer interfaces are systems that allow the control of external devices using the information extracted from the brain signals. Such systems find applications in rehabilitation, as an alternative communication channel and in multimedia applications for entertainment and gaming. In this work, a new approach based on the Time-Frequency (TF) distribution of the signal power, obtained by a...

A number of endmember extraction methods have been developed to identify pure pixels in hyperspectral images (HSIs). The majority them use only one spectrum represent kind material, which ignores the spectral variability problem that particularly characterizes a HSI with high spatial resolution. Only few algorithms multiple endmembers representing within each class, called bundle (EBE). This ar...

Feature selection techniques are often employed for reducing data dimensionality, improving computational efficiency, and most importantly selecting a subset of the important features model building. The present study explored utility Filter-Wrapper (FW) approach feature using terrestrial hyperspectral remote sensing imagery. efficacy FW was evaluated in conjunction with Random Forest (RF) Extr...