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Showing 1 to 20 of 56 for “"Data classification"”.
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An improved functional link neural network for data classification
The goal of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. The implementation of several classification models is challenging as some only work well when the underlying assumptions are satisfied. In order to …
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Techniques to improve dynamic cache management with static data classification
… use static information about how programs access data to manage the memory hierarchy. Static techniques are effective on regular programs, but because they set fixed policies, they are vulnerable to changes in program behavior or available cache space. Instead, most systems rely on dynamic caching …
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Improved cuckoo search based neural network learning algorithms for data classification
… algorithm (ABC-LM). Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. Overall from the simulation results, it is realized that the proposed CS based NN algorithms performs better than all other proposed and conventional …
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An improved data classification framework based on fractional particle swarm optimization
… Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven …
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Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine
This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive Boosting (AdaBoost) algorithm. AdaBoost is an effective solution for …
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Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning
… allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the …
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Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.
Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions …
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Evaluating data classification methods for choropleth maps in South Africa : a usability study
… communicate this spatial knowledge? Geospatial data visualisation techniques have evolved rapidly over the past decade. Today, most geographic information system (GIS) software has a plethora of built-in spatial analysis and visualisation techniques that enable users to quickly and effortlessly …
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The effect of pre-processing techniques and optimal parameters on BPNN for data classification
… problems such as pattern recognition and data analysis. It’s data-driven, self-adaptive, and non-linear capabilities channel it for use in processing at high speed and ability to learn the solution to a problem from a set of examples. It has been adequately applied in areas such as …
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Sensitive Multiplexed MicroRNA Spatial Profiling and Data Classification Framework Applied to Murine Breast Tumors
… We applied our methodology coupled with a data analysis pipeline to K14-Cre Brca1 superscript f/f Tp53 superscript f/f murine breast tumors to showcase the information gained from this approach.
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Combining adaptive and designed statistical experimentation : process improvement, data classification, experimental optimization and model building
… computer experiments and better post-experiment data analysis. The unifying concept of this thesis is to present and evaluate new ways of using adaptive experimentation combined with the traditional statistical experiment. The first application uses an adaptive experiment as a preliminary step to …
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DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION
… solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop …
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An Investigation Into a Hybrid Genetic Programming and Ant Colony Optimization Method for Credit Scoring
… Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO …
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Issues in Credit Risk Assessment in Agricultural Credit Markets
… model for default is compared with the actual data. Classification accuracy is utilized with the discrete models, while RMSE is used with the continuous models. The importance of the estimated models is also examined by the marginal effects of the explanatory variables on the probability of …
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Machine learning and autonomous system for human gait analysis based on walk speed
… been used for gait analysis systems, to perform data classification of gait pattern changes based on walking speeds. This system enables the tracking without the need of any markers. Moreover, the Kinect camera is considered a low-cost device, and is quick to install, even in an unprepared …
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A Model-Free Approach for Classification of fMRI Brain Images
… on functional magnetic resonance imaging (fMRI) data. Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for ensuring homogeneous research samples and for clinical diagnostic purposes. Unlike other …
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