scholarly journals Spatiotemporal Feature Selection Improves Prediction Accuracy of Multi-Voxel Pattern Classification

2019 ◽  
Author(s):  
Jeiran Choupan ◽  
Yaniv Gal ◽  
Pamela K. Douglas ◽  
Mark S. Cohen ◽  
David C. Reutens ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Classification Performance ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Spatiotemporal Feature ◽  
Multivariate Pattern ◽  
Single Time Point

AbstractThe importance of spatiotemporal feature selection in fMRI decoding studies has not been studied exhaustively. Temporal embedding of features allows the incorporation of brain activity dynamics into multivariate pattern classification, and may provide enriched information about stimulus-specific response patterns and potentially improve prediction accuracy. This study investigates the possibility of enhancing the classification performance by exploring spatial and temporal (spatiotemporal) domain, to identify the optimum combination of the spatiotemporal features based on the classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby et al. (2001) study. Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 seconds. A wide range of spatiotemporal observations was created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers, prediction accuracies for these combinations were then compared with the single time-point spatial multivariate pattern approach that uses only a single temporal observation. The results showed that on average spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent. As expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until ∼4 seconds after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design systematic and optimal approaches to the incorporation of spatiotemporal dependencies into feature selection for decoding.HighlightsSpatiotemporal feature selection effect on MVPC was assessed in slow event-related fMRISpatiotemporal feature selection improved brain decoding accuracyFrom ∼2-11 seconds after stimuli onset were the most informative part of each trialRandom forest outperformed support vector machinesRandom forest benefited more from temporal changes compared with support vector machine

Prediction of Breast Cancer Using Machine Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 901-908
Author(s):  
Somil Jain ◽  
Puneet Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Breast Cancer Dataset

Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

scholarly journals Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

2020 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Classification Performance ◽  
Support Vector ◽  
Product Reviews ◽  
Feature Selection Technique ◽  
Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

scholarly journals Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hayder Riyadh Mohammed Mohammed ◽  
Sumarni Ismail
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Structural Engineering ◽  
Resistance Mechanism ◽  
Longitudinal Axis ◽  
Statistical Correlation ◽  
Support Vector ◽  
Flexural Member

The shear and bending are the actions that are experienced in the beam owing to the fact that the beam is a flexural member due to the load in the transverse direction to their longitudinal axis. The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in the field of structural engineering. There have been several methodologies introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex characteristic of the resistance mechanism involving dowel effect of longitudinal reinforcement, concrete in the compression zone, contribution of the stirrups if existed, and the aggregate interlock. Hence, the current research proposed a new soft computing model called random forest (RF) to predict Vs. Experimental datasets were collected from the open-source literature including the related geometric properties and concrete characteristics of beam specimens. Nine input combinations were constructed based on the statistical correlation to be supplied for the proposed predictive model. The prediction accuracy of the RF model was validated against the Support Vector Machine (SVM), and several other empirical formulations have been adopted in the literature. The proposed RF model revealed better prediction accuracy in addition the model structure emphasis in the incorporation of seven predictors by excluding (beam flange thickness and coefficient). In the quantitative term, the minimal root mean square error value was attained (RMSE = 89.68 kN).

scholarly journals Machine Learning-Based Hourly Frost-Prediction System Optimized for Orchards Using Automatic Weather Station and Digital Camera Image Data

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 846
Author(s):  
Ilseok Noh ◽  
Hae-Won Doh ◽  
Soo-Ock Kim ◽  
Su-Hyun Kim ◽  
Seoleun Shin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Digital Camera ◽  
Image Data ◽  
Classification Performance ◽  
Classification Model ◽  
Support Vector ◽  
Observation Data ◽  
Freezing Resistance

Spring frosts damage crops that have weakened freezing resistance after germination. We developed a machine learning (ML)-based frost-classification model and optimized it for orchard farming environments. First, logistic regression, decision tree, random forest, and support vector machine models were trained using balanced Korea Meteorological Administration (KMA) Automated Synoptic Observing System (ASOS) frost observation data for March from the last 10 years (2008–2017). Random forest and support vector machine models showed good classification performance and were selected as the main techniques, which were optimized for orchard fields based on initial frost occurrence times. The training period was then extended to March–April for 20 years (2000–2019). Finally, the model was applied to the KMA ASOS frost observation data from March to April 2020, which were not used in the previous steps, and RGB data were extracted by digital cameras installed in an orchard in Gyeonggi-do. The developed model successfully classified 117 of 139 frost observation cases from the domestic ASOS data and 35 of 37 orchard camera observations. The assumption of the initial frost occurrence time for training helped the most in improving the frost-classification model. These results clearly indicate that the frost-classification model using ML has applicable accuracy in orchard farming.

scholarly journals A Semisupervised Feature Selection with Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Kun Dai ◽  
Hong-Yi Yu ◽  
Qing Li
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Learning Problems ◽  
Support Vector ◽  
Data Sets ◽  
Alternating Direction ◽  
Optimal Classification ◽  
Highly Correlated

Feature selection has proved to be a beneficial tool in learning problems with the main advantages of interpretation and generalization. Most existing feature selection methods do not achieve optimal classification performance, since they neglect the correlations among highly correlated features which all contribute to classification. In this paper, a novel semisupervised feature selection algorithm based on support vector machine (SVM) is proposed, termed SENFS. In order to solve SENFS, an efficient algorithm based on the alternating direction method of multipliers is then developed. One advantage of SENFS is that it encourages highly correlated features to be selected or removed together. Experimental results demonstrate the effectiveness of our feature selection method on simulation data and benchmark data sets.

scholarly journals Artificial Intelligence for Creating Low Latency and Predictive Intrusion Detection with Security Enhancement in Power Systems

2021 ◽  
Vol 11 (24) ◽  
pp. 11988
Author(s):  
Robin Singh Bhadoria ◽  
Naman Bhoj ◽  
Hatim G. Zaini ◽  
Vivek Bisht ◽  
Md. Manzar Nezami ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Latency ◽  
Machine Model ◽  
Time Accuracy

Advancement in network technology has vastly increased the usage of the Internet. Consequently, there has been a rise in traffic volume and data sharing. This has made securing a network from sophisticated intrusion attacks very important to preserve users’ information and privacy. Our research focuses on combating and detecting intrusion attacks and preserving the integrity of online systems. In our research we first create a benchmark model for detecting intrusions and then employ various combinations of feature selection techniques based upon ensemble machine learning algorithms to improve the performance of the intrusion detection system. The performance of our model was investigated using three evaluation metrics namely: elimination time, accuracy and F1-score. The results of the experiment indicated that the random forest feature selection technique had the minimum elimination time, whereas the support vector machine model had the best accuracy and F1-score. Therefore, conclusive evidence could be drawn that the combination of random forest and support vector machine is suitable for low latency and highly accurate intrusion detection systems.

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