TITLE:
A Novel Approach to Disqualify Datasets Using Accumulative Statistical Spread Map with Neural Networks (ASSM-NN)
AUTHORS:
Mahmoud Zaki Iskandarani
KEYWORDS:
Pattern Recognition, Informatics, Neural Networks, Data Mining, Classification, Prediction, Statistics
JOURNAL NAME:
Intelligent Information Management,
Vol.7 No.3,
May
27,
2015
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.