The Classification of Data A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization

Tripathi, Kshitij; Vyas, Rajendra G.; Gupta, Anil Kumar

doi:10.26438/ijcse/v6i5.241254

Cited by 3 publications

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“…The proposed methodology is based on n-fold trainingvalidation-test approach which is combination of n-fold cross-validation with validation [21]. In n-fold crossvalidation the dataset is initially divided into 'n' folds.…”

Section: Proposed Methodologymentioning

confidence: 99%

Document Classification Using Artificial Neural Network

Tripathi

Vyas

Gupta

2019

AJCST

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The Document classification system is the field of data mining in which the format of data is based on bag of words (BoW) or document vector model and the task is to build a machine which after successfully learn the characteristic of given data set, predicts the category of the document to which the word vector belongs. In this approach document is represented by BoW where every single word is used as feature which occurs in a document. The proposed article presents artificial neural network approach which is hybrid of n-fold cross validation and training-validation-test approach for classification of data.

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Section: Proposed Methodologymentioning

confidence: 99%

Document Classification Using Artificial Neural Network

Tripathi

Vyas

Gupta

2019

AJCST

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“…So for serving maximum exposure to both classes, we are doing 15 fold cross-validation and each time 13 folds are used for training and 14th and 15th fold are used respectively for validation and testing in each experiment and each set is acting as test set. It is also revealed from K-fold TVT [9]( table 1) approach that the solution of the problem of over-fitting in ANN is hidden in the data itself i.e. we are discovering most friendly validation set to the test set to avoid over-fitting.…”

Section: Classification Through K-fold Tvt Approachmentioning

confidence: 99%

Diabetes Classification and Prediction Using Artificial Neural Network

Tripathi¹

2019

IJCET

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The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.

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“…They have the unreasonable capability to establish a relationship between the input and output data using mathematical techniques where the data format must be numeric. ANNs deliver excellent results in the classification of data [ 1 , 2 , 3 ], which come under supervised learning. In this article, we explore Kohonen's SOM as well as their one‐ (1D) and two‐dimensional (2D) clustering spaces and observe that they are also capable of giving excellent results in clustering.…”

Section: Introductionmentioning

confidence: 99%

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Tripathi

2024

Health Care Science

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IntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

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The Classification of Data A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization

Cited by 3 publications

References 0 publications

Document Classification Using Artificial Neural Network

Document Classification Using Artificial Neural Network

Diabetes Classification and Prediction Using Artificial Neural Network

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

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