Abstract
A novel approach to detect and filter out an unhealthy dataset from a matrix of datasets is developed, tested, and proved. The technique employs a new type of self organizing map called Accumulative Statistical Spread Map (ASSM) to establish the destructive and negative effect a dataset will have on the rest of the matrix if stayed within that matrix. The ASSM is supported by training a neural network engine, which will determine which dataset is responsible for its inability to learn, classify and predict. The carried out experiments proved that a neural system was not able to learn in the presence of such an unhealthy dataset that possessed some deviated characteristics, even though it was produced under the same conditions and through the same process as the rest of the datasets in the matrix, and hence, it should be disqualified, and either removed completely or transferred to another matrix. Such novel approach is very useful in pattern recognition of datasets and features that do not belong to their source and could be used as an effective tool to detect suspicious activities in many areas of secure filing, communication and data storage.
Highlights
Many neural networks applications are concerned with analyzing issues related to pattern recognition by using a supervised training method with training datasets
The approach uses a new Accumulative Statistical Spread Map (ASSM) to initially establish the coherence of the patterns under consideration, and will not cause a misclassification in the neural network, and when the status is established, the neural structure is used to determine which of the datasets and patterns is causing such misclassification and raising the error rate
Input ASSM = ∑ Token j i =1 where f: Correlation function between the Tokens; n: Range of classification; j: Range of Tokens; The ASSM carries out initial re-organization and sorting by correlation according to Equation (3), before it produces the final output: ASSM Organization = Corr ( Categoryi, Input ASSM )
Summary
Many neural networks applications are concerned with analyzing issues related to pattern recognition by using a supervised training method with training datasets.
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