A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks

2020 ◽  
pp. 52-68
Author(s):  
Srijan Das ◽  
Arpita Dutta ◽  
Saurav Sharma ◽  
Sangharatna Godboley
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Anomaly Detection ◽  
Supervised Classification ◽  
Detection Algorithm ◽  
Classification Problems ◽  
Anomalous Data ◽  
Criterion Functions ◽  
The Neural Networks ◽  
Data Objects

Anomaly Detection is an important research domain of Pattern Recognition due to its effects of classification and clustering problems. In this paper, an anomaly detection algorithm is proposed using different primitive cost functions such as Normal Perceptron, Relaxation Criterion, Mean Square Error (MSE) and Ho-Kashyap. These criterion functions are minimized to locate the decision boundary in the data space so as to classify the normal data objects and the anomalous data objects. The authors proposed algorithm uses the concept of supervised classification, though it is very different from solving normal supervised classification problems. This proposed algorithm using different criterion functions has been compared with the accuracy of the Neural Networks (NN) in order to bring out a comparative analysis between them and discuss some advantages.

A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks

2017 ◽  
Vol 4 (4) ◽  
pp. 1-16
Author(s):  
Srijan Das ◽  
Arpita Dutta ◽  
Saurav Sharma ◽  
Sangharatna Godboley
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Anomaly Detection ◽  
Supervised Classification ◽  
Detection Algorithm ◽  
Classification Problems ◽  
Anomalous Data ◽  
Criterion Functions ◽  
The Neural Networks ◽  
Data Objects

Anomaly Detection is an important research domain of Pattern Recognition due to its effects of classification and clustering problems. In this paper, an anomaly detection algorithm is proposed using different primitive cost functions such as Normal Perceptron, Relaxation Criterion, Mean Square Error (MSE) and Ho-Kashyap. These criterion functions are minimized to locate the decision boundary in the data space so as to classify the normal data objects and the anomalous data objects. The authors proposed algorithm uses the concept of supervised classification, though it is very different from solving normal supervised classification problems. This proposed algorithm using different criterion functions has been compared with the accuracy of the Neural Networks (NN) in order to bring out a comparative analysis between them and discuss some advantages.

scholarly journals Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
G. López-Vázquez ◽  
M. Ornelas-Rodriguez ◽  
A. Espinal ◽  
J. A. Soria-Alcaraz ◽  
A. Rojas-Domínguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Supervised Classification ◽  
Fitness Function ◽  
Statistical Tests ◽  
Search Space ◽  
Spiking Neural Networks ◽  
Classification Problems ◽  
Benchmark Datasets ◽  
Explicit Training ◽  
Better Than

This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal performs the SNN design by exploring the search space of three-layered feedforward topologies with configured synaptic connections (weights and delays) so that no explicit training is carried out. Besides, the designed SNNs have partial connections between input and hidden layers which may contribute to avoid redundancies and reduce the dimensionality of input feature vectors. The proposal was tested on several well-known benchmark datasets from the UCI repository and statistically compared against a similar design methodology for second generation ANNs and an adapted version of that methodology for SNNs; also, the results of the two methodologies and the proposed one were improved by changing the fitness function in the design process. The proposed methodology shows competitive and consistent results, and the statistical tests support the conclusion that the designs produced by the proposal perform better than those produced by other methodologies.

scholarly journals Review and comparative analysis of machine learning libraries for machine learning

2019 ◽  
Vol 27 (4) ◽  
pp. 305-315
Author(s):  
Migran N. Gevorkyan ◽  
Anastasia V. Demidova ◽  
Tatiana S. Demidova ◽  
Anton A. Sobolev
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Programming Languages ◽  
Multilayer Perceptron ◽  
Tabular Form ◽  
Training Time ◽  
Current State ◽  
Different Types ◽  
The Neural Networks

The article is an overview. We carry out the comparison of actual machine learning libraries that can be used the neural networks development. The first part of the article gives a brief description of TensorFlow, PyTorch, Theano, Keras, SciKit Learn libraries, SciPy library stack. An overview of the scope of these libraries and the main technical characteristics, such as performance, supported programming languages, the current state of development is given. In the second part of the article, a comparison of five libraries is carried out on the example of a multilayer perceptron, which is applied to the problem of handwritten digits recognizing. This problem is well known and well suited for testing different types of neural networks. The study time is compared depending on the number of epochs and the accuracy of the classifier. The results of the comparison are presented in the form of graphs of training time and accuracy depending on the number of epochs and in tabular form.

scholarly journals BIBLIOMETRIC MAPPING OF THE COMPUTATIONAL INTELLIGENCE FIELD

2007 ◽  
Vol 15 (05) ◽  
pp. 625-645 ◽  
Author(s):  
NEES JAN VAN ECK ◽  
LUDO WALTMAN
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Optimization Problems ◽  
Control Problems ◽  
Classification Problems ◽  
Current Structure ◽  
The Neural Networks ◽  
Regression Problems

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

scholarly journals Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research (Preprint)

2021 ◽  
Author(s):  
Vendula Churová ◽  
Roman Vyškovský ◽  
Kateřina Maršálová ◽  
David Kudláček ◽  
Daniel Schwarz
Keyword(s):  
Anomaly Detection ◽  
Clinical Research ◽  
Data Quality ◽  
Real World ◽  
Detection Algorithm ◽  
Data Detection ◽  
Real World Data ◽  
World Data ◽  
Clinical Registries ◽  
Anomalous Data

BACKGROUND Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also of being mishandled by investigators. OBJECTIVE The urgent need to assure the highest data quality possible has led to implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. METHODS An automatic anomaly detection algorithm based on machine learning that combines clustering with a series of distance metrics is presented. RESULTS The algorithm is built in a particular electronic data capture (EDC) system that stores real-world data in clinical registries. These data, together with newly generated, simulated anomalous data were utilized to evaluate the detection performance of this algorithm. CONCLUSIONS The experimental results demonstrate that the algorithm, which is universal, and as such may be implemented in other EDC systems, is capable of anomalous data detection with sensitivity exceeding 85%.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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