A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Feature selection <s> The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Feature selection <s> A dual classification (DuC) approach is presented to deal with the potential drawback of instance selection.During training, two classifiers are trained by a 'good' and 'noisy' sets respectively after performing instance selection.Experiments are conducted used 50 small scale and 4 large scale datasets.The results show that the DuC approach outperforms three state-of-the-art instance selection algorithms. Instance selection aims at filtering out noisy data (or outliers) from a given training set, which not only reduces the need for storage space, but can also ensure that the classifier trained by the reduced set provides similar or better performance than the baseline classifier trained by the original set. However, since there are numerous instance selection algorithms, there is no concrete winner that is the best for various problem domain datasets. In other words, the instance selection performance is algorithm and dataset dependent. One main reason for this is because it is very hard to define what the outliers are over different datasets. It should be noted that, using a specific instance selection algorithm, over-selection may occur by filtering out too many 'good' data samples, which leads to the classifier providing worse performance than the baseline. In this paper, we introduce a dual classification (DuC) approach, which aims to deal with the potential drawback of over-selection. Specifically, performing instance selection over a given training set, two classifiers are trained using both a 'good' and 'noisy' sets respectively identified by the instance selection algorithm. Then, a test sample is used to compare the similarities between the data in the good and noisy sets. This comparison guides the input of the test sample to one of the two classifiers. The experiments are conducted using 50 small scale and 4 large scale datasets and the results demonstrate the superior performance of the proposed DuC approach over the baseline instance selection approach. <s> BIB002
Feature selection is another relevant method which allows a dimensionality reduction of feature set numbers from the extracted feature in smartphone sensor data, used to monitor the individual activity of participant in a crowd. To avoid the high cost of computation, for the easy and fast decision process, the technique has a tendency to improve ARAC BIB001 BIB002 . The challenges are the problems discussed in Sections 6.1.1-6.1.1.6 which are results of SLR in Section 4 including the issue of inaccuracy in AR which has not been completely addressed. Examples of feature selection applied in AR are genetic algorithm, COR and information gain. However, developing valid classification (or prediction) model with feature selection is usually the key to success in most data mining, AR and pattern recognition problems BIB001 BIB002 .