Complex Classification Algorithms Research Articles

Retracted: Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening

Retracted: Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening

LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers

In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android operating system, are extracted through static analysis, and security analyzes of applications are carried out with machine learning techniques. Based on the multiple linear regression techniques, two classifiers are proposed for permission-based Android malware detection. These classifiers are compared on four different datasets with basic machine learning techniques such as support vector machine, k-nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging method, which is one of the ensemble learning, different classifiers are created, and the classification performance is increased. As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening

Screening and classification of characteristic genes is a complex classification problem, and the characteristic sequences of gene expression show high-dimensional characteristics. How to select an effective gene screening algorithm is the main problem to be solved by analyzing gene chips. The combination of KNN, SVM, and SVM-RFE is selected to screen complex classification problems, and a new method to solve complex classification problems is provided. In the process of gene chip pretreatment, LogFC and P value equivalents in the gene expression matrix are screened, and different gene features are screened, and then SVM-RFE algorithm is used to sort and screen genes. Firstly, the characteristics of gene chips are analyzed and the number between probes and genes is counted. Clustering analysis among each sample and PCA classification analysis of different samples are carried out. Secondly, the basic algorithms of SVM and KNN are tested, and the important indexes such as error rate and accuracy rate of the algorithms are tested to obtain the optimal parameters. Finally, the performance indexes of accuracy, precision, recall, and F1 of several complex classification algorithms are compared through the complex classification of SVM, KNN, KNN-PCA, SVM-PCA, SVM-RFE-SVM, and SVM-RFE-KNN at P=0. 01,0.05,0.001. SVM-RFE-SVM has the best classification effect and can be used as a gene chip classification algorithm to analyze the characteristics of genes.

A Computationally Efficient sEMG based Silent Speech Interface using Channel Reduction and Decision Tree based Classification

Abstract Silent Speech Interface is one of the promising areas of Human-Computer Interaction research. The surface electromyography based silent speech interface is a technique where the electric activity of facial muscles are used to detect speech. The existing sEMG based SSI techniques use complex machine learning algorithms and too many number of electrodes on the subject’s face. It creates inconvenience to the user who might have undergone laryngectomy. More number of electrodes becomes highly invasive to the user, while complex classification algorithms increase the computational cost and prevents real time implementation of sEMG based SSI. Thus the objective of this research work was to develop a less complex and computationally less expensive model to classify words. To achieve this goal channel reduction technique and the use of Decision Tree based classification algorithm was employed. Only the time domain features are used as input to the classification algorithm. The motive was to exploit the advantage of computational ease in extracting the time domain features as compared to the frequency domain features. The sEMG data of the words used in this work are obtained from the complete utterance of the sentences and not by individual utterances of the word. Our algorithm was able to achieve a word accuracy of 95.17% even after applying a channel reduction, thereby allowing us to use only the data of 5 channels, in place of a conventional seven channel setup.

To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Intelligent method for sensor subset selection for machine olfaction

A fundamental design concept for an array of sensors used in machine olfaction devices, electronic noses (e-noses), is that each sensor should maximize the overall sensitivity and provides different selectivity profiles over the range of target odor application. Ideally, each sensor should produce a different response to a given odor so that a unique odor pattern is created. Since this is rarely the case, sensor selection or reduction is needed when classification performance, cost, and technology limitations are issues of concern. The first step in the selection/reduction process is to generate features from each sensor's output waveform. In practice, some of the features obtained from an array of sensors are redundant and irrelevant due to cross-sensitivity and odor characteristics. As a result, inappropriate features or a poor configuration of features can lead to a deterioration of classification performance, or a more complex classification algorithm may be required. Hence, sensor selection for e-nose systems is of great important. In this study, a novel computationally efficient method is introduced by selecting the first few critical sensors based on a maximum margin criterion among different odor classes. Then, a stochastic search algorithm, a genetic algorithm (GA), uses those features as an initial step to optimize our sensor selection problem. The advantages of the proposed method are not only to avoid any initial misstep starting the search, but also to reduce the searching space for the optimal sensor array. From the experimental results on coffee and soda data sets, the number of selected sensors is significantly reduced (up to 90%) and classification performance is near 100%.

Propensity Scores

Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.