106,940 research outputs found
Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach
In this paper, a short-term load forecasting approach based network
reconfiguration is proposed in a parallel manner. Specifically, a support
vector regression (SVR) based short-term load forecasting approach is designed
to provide an accurate load prediction and benefit the network reconfiguration.
Because of the nonconvexity of the three-phase balanced optimal power flow, a
second-order cone program (SOCP) based approach is used to relax the optimal
power flow problem. Then, the alternating direction method of multipliers
(ADMM) is used to compute the optimal power flow in distributed manner.
Considering the limited number of the switches and the increasing computation
capability, the proposed network reconfiguration is solved in a parallel way.
The numerical results demonstrate the feasible and effectiveness of the
proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and
Computers 201
Application of Synthetic Informative Minority Over-Sampling (SIMO) Algorithm Leveraging Support Vector Machine (SVM) On Small Datasets with Class Imbalance
Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et al., 2017). Wide range of Class Imbalanced learning techniques are introduced to overcome the above-mentioned problems, although each has some advantages and shortcomings. This paper provides details on the behavior of a novel imbalanced learning technique Synthetic Informative Minority Over-Sampling (SIMO) Algorithm Leveraging Support Vector Machine (SVM) on small datasets of records less than 200. Base classifiers, Logistic regression and SVM is used to validate the impact of SIMO on classifier’s performance in terms of metrices G-mean and Area Under Curve. A Comparison is derived between SIMO and other algorithms SMOTE, Smote-Borderline, ADAYSN to evaluate performance of SIMO over others
Comparison of Classifiers Models for Prediction of Intimate Partner Violence
Intimate partner violence (IPV) is a problem that has been studied by different researchers to determine the factors that influence its occurrence, as well as to predict it. In Peru, 68.2% of women have been victims of violence, of which 31.7% were victims of physical aggression, 64.2% of psychological aggression, and 6.6% of sexual aggression. Therefore, in order to predict psychological, physical and sexual intimate partner violence in Peru, the database of denouncements registered in 2016 of the “Ministerio de la Mujer y Poblaciones Vulnerables” was used. This database is comprised of 70510 complaints and 236 variables concerning the characteristics of the victim and the aggressor. First of all, we used Chi-squared feature selection technique to find the most influential variables. Next, we applied the SMOTE and random under sampling techniques to balance the dataset. Then, we processed the balanced dataset using cross validation with 10 folds on Multinomial Logistic Regression, Random Forest, Naive Bayes and Support Vector Machines classifiers to predict the type of partner violence and compare their results. The results indicate that the Multinomial Logistic Regression and Support Vector Machine classifiers performed better on different scenarios with different feature subsets, whereas the Naïve Bayes classifier showed inferior. Finally, we observed that the classifiers improve their performance as the number of features increased
Unconventional machine learning of genome-wide human cancer data
Recent advances in high-throughput genomic technologies coupled with
exponential increases in computer processing and memory have allowed us to
interrogate the complex aberrant molecular underpinnings of human disease from
a genome-wide perspective. While the deluge of genomic information is expected
to increase, a bottleneck in conventional high-performance computing is rapidly
approaching. Inspired in part by recent advances in physical quantum
processors, we evaluated several unconventional machine learning (ML)
strategies on actual human tumor data. Here we show for the first time the
efficacy of multiple annealing-based ML algorithms for classification of
high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas.
To assess algorithm performance, we compared these classifiers to a variety of
standard ML methods. Our results indicate the feasibility of using
annealing-based ML to provide competitive classification of human cancer types
and associated molecular subtypes and superior performance with smaller
training datasets, thus providing compelling empirical evidence for the
potential future application of unconventional computing architectures in the
biomedical sciences
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