Categorizing the Document Using Multi Class Classification in Data Mining

Joshi, Shweta; Nigam, Bhawna

doi:10.1109/cicn.2011.50

Cited by 20 publications

(14 citation statements)

References 12 publications

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“…Classification can be used for a variety of issue approaches, such as IR (Information Retrieval) (Joshi & Nigam, 2011), Geography and Remote Sensing (Zhong et al, 2014), Web Technology (Ali, Shamsuddin, & Ismail, 2012), and others. In the process, training and testing require variable, which is commonly called feature.…”

Section: Classification Modelmentioning

confidence: 99%

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

Fitrianah

Fahmi

2019

Sinergi

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This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.

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Section: Classification Modelmentioning

confidence: 99%

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

Fitrianah

Fahmi

2019

Sinergi

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“…It aims to automatically group similar documents in one cluster using different types of extractions and cluster algorithms. There are ongoing works done to improve Document Clustering techniques such as Extractions and Clustering in significant data analysis [8,9] approaches to overcome the difficulty in designing a general purpose document clustering for crime investigation [14]and the ill-posed problem of extraction and clustering. In real cases recently linear regression model is used to make future crime prediction of various crimes for Delhi city [15].…”

Section: Related Workmentioning

confidence: 99%

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Begam¹,

Sengottuvelan²

2018

IJET

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Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule mining algorithm to solve the crime problems. Also to use the sentence case observer technique for knowledge discovery from the crime records for proper case identification from sentence case records and to help increase the predictive accuracy. Crime examines a developing method and identifying the field in law implementation without standard definitions for correct judgment. With the expanding utilization of the clustering automated frameworks to track crimes, information examiners helping the law implementation officers and analysts to accelerate the way toward measuring crimes. The main contribution is to analyses the attribute case with relative sentences of count word analyzes factor for improving the crime prediction for categorizing crime type.

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“…Meanwhile, it has also driven the development of cancer classification technology by using gene expression profiles. Since the measures of the genes expression involves thousands of genes or more, classification has attracted more and more attentions, which also has become a popular research topic in learning techniques [1], [2]. As a result of the development, there are many important applications such as cancer classification.…”

Section: Introductionmentioning

confidence: 99%

A horizon-vertical dimension reduction method for classification of cancer cell gene in a fuzzy neural network

Zhang

Chen

2013

2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)

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Because gene expression profiles in normal cells are different from those of cancer cells, experimental results of the tests can diagnose a cancerous person. However, the gene data are usually with high variable dependent, high dimensional, and very noisy. It is not appropriate to use the original data to train or to test the forecasting model. Depending on the unique properties of the genes expression data, a new statistical dimension reduction method called horizon-vertical dimension reduction method (HVDRM) is developed in this paper. The feature set dimension is reduced from 2000 to 5 by applying HVDRM. Then, the extracted feature set is arranged to train in an artificial neural network (ANN) and a fuzzy neural network (FNN). Keep these two trained models, which is then send to the classification system to examine whether the testing sample is normal or not. Three kinds of experiments are conducted to test the validity, namely, original data for an ANN, reduction feature data for an ANN, and reduced feature data for a FNN. It is found that the testing accuracy of the FNN has the best result. It is concluded that the proposed HVDRM is an effective method to extract feature data and the FNN is more suitable than ANN in the given cancer cell gene detection as the forecasting model.

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Categorizing the Document Using Multi Class Classification in Data Mining

Cited by 20 publications

References 12 publications

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

A horizon-vertical dimension reduction method for classification of cancer cell gene in a fuzzy neural network

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