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Sensorimotor rhythms-based Brain-Computer Interfaces (BCIs) have successfully been employed to address upper limb motor rehabilitation after stroke. In this context, becomes crucial the choice of features that would enable an appropriate electroencephalographic (EEG) sensorimotor activation/engagement underlying the favourable motor recovery. Here, we present a novel feature selection algorithm (GUIDER) designed and implemented to integrate specific requirements related to neurophysiological knowledge and rehabilitative principles. The GUIDER algorithm was tested on an EEG dataset collected from 13 subacute stroke participants. The comparison between the automatic feature selection procedure by means of GUIDER algorithm and the manual feature selection executed by an expert neurophysiologist returned similar performance in terms of both feature selection and classification. Our preliminary findings suggest that the choices of experienced neurophysiologists could be reproducible by an automatic approach. The proposed automatic algorithm could be apt to support the professional end-users not expert in BCI such as therapist/clinicians and, to ultimately foster a wider employment of the BCI-based rehabilitation after stroke.

Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distribution network will bring great security challenges to data transmission. The existing data security methods, such as access control and encryption, address the security of massive, high dimensional, and non-text data in the active distribution network. Therefore, feature selection algorithm based on rough set is first given to reduce the complexity of massive and high dimensional data. And then, based on feature selection, the authors propose a data filtering function model mining algorithm by using gene expression programming (DFFM-FSGEP). Finally, to solve the data filter function model mining of the incremental dataset, they also present an incremental mining algorithm of the filtering function model based on functional fitting (IMFFM-FF). Experimental results show that the proposed algorithm in this study can greatly reduce the complexity of experimental datasets to be processed, and compared with the other algorithms, DFFM-FSGEP has higher classification accuracy and sensitivity, and IMFFM-FF has higher classification speed and classification accuracy for incremental datasets.

The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording the ripple curve are noted in the published materials. This study proposes a new hydrocuff method for hemodynamic parameters and blood pressure values measuring. A block diagram of the device being developed is presented. Algorithms for processing the pulse wave contour are presented. A neural network applying necessity for the multiparametric feature space formation is substantiated. The pulse wave contours obtained using hydrocuff technology of oscillation formation for various age groups are presented. According to preliminary estimates, by the moment of the dicrotic surge formation, it is possible to judge the ratio of the heart and blood vessels work, which makes it possible to form an expanded feature space of significant parameters based on neural network classifiers. This study presents the characteristics accounted for creating a database for training a neural network.

Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely Educational Data Mining (EDM). How the Feature Selection algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the Feature Selection (FS) algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding Feature Selection algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions.

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories; Classification, Feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. Feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCI's datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms.

Feature selection is an important prerequisite of any pattern recognition, machine learning or data mining problem. A lot of algorithms for feature subset selection have been developed so far for reduction of dimensionality of the data set in order to achieve high recognition accuracy with low computational cost. However, some methods or algorithms work well for some of the data sets and perform poorly on others. For any particular data set, it is difficult to find out the most suitable algorithm without some random trial and error process. It seems that the characteristics of the data set might have some effect on the algorithm for feature selection. In this work, the data set characteristics is studied for recommendation of appropriate feature selection algorithm to be used for a particular data set. A new proposal in terms of intra attribute relationship and a measure MVS (multivariate score) has been introduced to quantify and group different data sets on the basis of the data set correlation structure into several categories. The measure is used to group 63 publicly available bench mark data set according to their characteristics. The performance of different feature selection algorithms on different groups of data are then studied by simulation experiments to verify the relationship o f data set characteristics and the feature selection algorithm. The effect of some other data set characteristics has also been studied. Finally a framework of recommendation regarding the choice of proper feature selection algorithm has been indicated.

Background Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. Results In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples. Conclusions Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings. Reviewers This article was reviewed by Leonid Hanin (nominated by Dr. Lev Klebanov), Limsoon Wong and Jun Yu. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0051-z) contains supplementary material, which is available to authorized users.

Departamento de Eletronica e Sistemas, Universidade Federal de Pernambuco,Recife,Brazil {andrea.bottino, matteo.desimone, aldo.laurentini,tiago.figueiredo}@polito.it Keywords: Kinship verification, Support Vector Machines, Principal Component Analysis, Feature Selection Algorithm. Abstract: Human face conveys to other human beings, and potentially to computers, much information such as identity, emotional states, intentions, age and attractiveness. Among this information there are kinship clues. Face kinship signals, as well as the human capabilities of capturing them, are studied by psychologist and sociologists. In this paper we present a new research aimed at analyzing, with image processing/pattern analysis techniques, facial images for detecting objective elements of similarity between siblings. To this end, we have constructed a database of high quality pictures of pairs of siblings, shot in controlled conditions, including frontal, profile, expressionless and smiling face images. A first analysis of the database has been performed using a commercial identity recognition software. Then, for discriminating siblings, we combined eigenfaces, SVM and a feature selection algorithm, obtaining a recognition accuracy close to that of a human rating panel.