Feature selection in accident data: an analysis of its application in classification algorithms

Sarkar, Amrita; Sahoo, G.; Sahoo, Umesh Chandra

doi:10.1504/ijdats.2016.077484

Cited by 8 publications

(2 citation statements)

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“…Without feature selection, classification of text data is incomplete because selection of good features strengthen the performance of a classifier. Much research work has been done on feature selection (Kalaivani and Shunmuganathan, 2016;Lin and Chen, 2012;Sarkar, 2016;Lee and Kim, 2015;Seetha et al, 2015;Roul et al, 2015Roul et al, , 2016aMokeddem et al, 2016;Azam and Yao, 2012;Roul et al, 2016b). Information theory-based feature selection is very effective in terms of computational cost, dimensionality, scalability and classifier's independence.…”

Section: Literature Reviewmentioning

confidence: 99%

Suitability and importance of deep learning feature space in the domain of text categorisation

Roul

2019

IJCISTUDIES

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One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.

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Section: Literature Reviewmentioning

confidence: 99%

Suitability and importance of deep learning feature space in the domain of text categorisation

Roul

2019

IJCISTUDIES

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“…Gunavathi and Premalatha (2015) used cuckoo search optimisation techniques for finding best features from the original dataset. Sarkar and Sahoo (2016) proposed ensemble approach for selecting best features among original features. Zheng et al (2010) used feature clustering techniques for finding best subset features.…”

Section: Introductionmentioning

confidence: 99%

Feature selection and instance selection using cuttlefish optimisation algorithm through tabu search

Karunakaran¹,

Suganthi²,

Rajasekar³

2020

IJENM

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Over the recent decades, the amount of data generated has been growing exponentially, the existing machine learning algorithms are not feasible for processing of such huge amount of data. To solve such kind of issues, we have two commonly adopted schemes, one is scaling up the data mining algorithms and other one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this paper, cuttlefish optimisation algorithm along with tabu search approach is used for data reduction. Dataset can be reduced mainly in two ways, one is the selecting optimal subset of features from the original dataset, in other words eliminating those features which are contributing lesser information another method is selecting optimal subset of instances from the original data set, in other words eliminating those instances which are contributing lesser information. Cuttlefish optimisation algorithm with tabu search finds both optimal subset of features and instances. Optimal subset of feature and instance obtained from the cuttlefish algorithm with tabu search provides a similar detection rate, accuracy rate, lesser false positive rate and the lesser computational time for training the classifier that we obtained from the original data set.

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Text Categorization Using a Novel Feature Selection Technique Combined with ELM

Roul

Sahoo

2018

Advances in Intelligent Systems and Computing

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Feature selection in accident data: an analysis of its application in classification algorithms

Cited by 8 publications

References 13 publications

Suitability and importance of deep learning feature space in the domain of text categorisation

Suitability and importance of deep learning feature space in the domain of text categorisation

Feature selection and instance selection using cuttlefish optimisation algorithm through tabu search

Text Categorization Using a Novel Feature Selection Technique Combined with ELM

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