Novel Static Multi-Layer Forest Approach and Its Applications

Bhagwat, Ganesh; Kumari, Shristi; Patekar, Vaishnavi; Deaconu, Adrian

doi:10.3390/math9212650

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Using dynamic programming highest depth general tree, which is unique, is obtained. Forest traversal techniques (15) are applied to get equivalent binary trees, and then binary trees are converted into full binary trees by introducing dummy nodes. Subsequently, tree traversal techniques are employed to obtain the preorder and sequences of binary trees.…”

Section: Proposed Workmentioning

confidence: 99%

Classification and Recognition of Bilingual Text Using Graph Edit Distance Based Degree of Similarity

Roopa¹,

Mahantesh²

2022

IJST

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Objectives: Graph Edit distance-based classification and recognition method is introduced in this study for bilingual characters. Specifically, this method aims to classify characters first and then recognize them in the 2nd level. Methods: This study combines both exact graph matching and inexact graph matching techniques to achieve better Recognition. The exact graph matching technique classifies characters by considering the number of vertices and edges as features to classify. Inexact graph matching uses an algorithmic model to measure the degree of similarity in the length of edit operations. Findings: The proposed model can compute, if not optimal, sequences all the time, at least near-optimal edit sequence. The proposed model is based on dynamic programming. Edit distance is estimated by comparing preorder sequences of the respective binary trees. Finally, this model uses the loss function to recognize text accurately. To bring out the practicality of the proposed model, we present a case study on the classification of English alphabets and Kannada conjunct consonants. Novelty: (i) A hybrid approach for converting a large class classification problem into a small classification problem that takes advantage of both exact and inexact graph matching techniques. (ii) An algorithmic model is designed to represent each character as a unique string. (iii) Uses dynamic programming in the right places to get the optimal tree, and (iv) to convert the graph matching problem to a string matching problem reduces the time complexity of the problem.

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Section: Proposed Workmentioning

confidence: 99%

Classification and Recognition of Bilingual Text Using Graph Edit Distance Based Degree of Similarity

Roopa¹,

Mahantesh²

2022

IJST

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“…Regression problems can be solved with the prediction ability of decision trees by categorization of the objects from insight of the data. The applications of decision trees have been considered to store hierarchical data, folder structure, organizational structure and XML/HTML data [1]. Decision trees in data mining are mostly used for operations research, decision analysis, identification of the strategy to reach the goal of the objective.…”

Section: Introductionmentioning

confidence: 99%

The Application of Decision Tree Method for Data Mining

G¹,

Ravishankar²

2022

TTIDMKD

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The study showcases the impact of data mining considering the application of decision trees that helps to develop data from large number of datasets. Linear data set has been generated through the model of decision trees. Secondary data collection method has been selected in this study with inductive approach. Cross sectional research design has been used in this study to derived insight of the subject of the study. Themes are developed using the peer reviewed journals published after 2019 and secondary collected data has been interpreted in this study to meet the goal of the subject. Implications of data mining including decision trees and decision rules in different field have been discussed over here with positive perspective approach. Customization of data mining in creativity also has been focused here. Different types of decision trees methods have been depicted here with comparison vision. Advantages and disadvantages of decision trees also have been discussed over here to evaluate the actual impact of application of decision trees in data mining. Interpretation of results also has been depicted here to analyse the consequences of decision trees in data mining to conclude the study with informative justification.

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