An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

Adamović, Saša; Miškovic, Vladislav; Maček, Nemanja; Milosavljević, Milan; Šarac, Marko; Saračević, Muzafer; Gnjatović, Milan

doi:10.1016/j.future.2020.01.056

Cited by 52 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The that yields is no better than a model that randomly classifies inputs into categories. This is interesting because in the next study we cover [ 23 ], the authors use data with features that are derived from heterogeneous numerical data. In that study two algorithms related to , Sequential Minimal Optimization ( ) (which is the name for the implementation in the Weka [ 68 ] library), and Support Vector Classification ( ) [ 15 ] yield the best performance in terms of multiple different metrics for one experiment.…”

Section: Catboost Applications By Fieldmentioning

confidence: 99%

“…Adamovic et al study is titled “An efficient novel approach for iris recognition based on stylometric features and machine learning techniques.” [ 23 ] Stylometry is the study of identifying an author based on the content of his or her work. In their study, stylometry is applied to the Base-64 encoding of iris images as though it were prose, so that identifying the hypothetical author equates to identifying the owner of the iris.…”

Section: Catboost Applications By Fieldmentioning

confidence: 99%

“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [ 18 ], Finance [ 19 – 22 ], Medicine [ 23 – 26 ], Biology [ 27 , 28 ], Electrical Utilities Fraud [ 29 – 31 ], Meteorology [ 32 , 33 ], Psychology [ 34 , 35 ], Traffic Engineering [ 7 , 36 ], Cyber-security [ 37 ], Bio-chemistry [ 5 , 38 ], and Marketing [ 39 ]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CatBoost for big data: an interdisciplinary review

2020

View full text Add to dashboard Cite

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

show abstract

Section: Catboost Applications By Fieldmentioning

confidence: 99%

Section: Catboost Applications By Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CatBoost for big data: an interdisciplinary review

2020

View full text Add to dashboard Cite

show abstract

“…The AUC that SVM yields is no better than a model that randomly classifies inputs into categories. This is interesting because in the next study we cover [4], the authors use data with features that are derived from heterogeneous numerical data. In that study two algorithms related to SVM, Sequential Minimal Optimization (SMO) (which is the name for the SVM implementation in the Weka [86] ML library), and Support Vector Classification (SVC) [14] yield the best performance in terms of multiple different metrics for one experiment.…”

Section: Xgbmentioning

confidence: 99%

“…Class Y has 450 samples, and Class N has 2,415 samples. The link to the CASIA dataset [4] containing the iris data is provided, but at the time of this writing, this site is not accessible. Adamovic et al write that they use the Recursive Feature Elimination (RFE) [39] and Regularized Random Forest (RRF) [27] methods for feature extraction.…”

Section: Xgbmentioning

confidence: 99%

CatBoost for Big Data: an Interdisciplinary Review

Hancock

Khoshgoftaar

2020

Preprint

View full text Add to dashboard Cite

Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data, Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have ellCcessfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that .55 CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

show abstract

LBPX: A Novel Feature Extraction Method for Iris Recognition

Podder

Mondal

2021

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

Cited by 52 publications

References 27 publications

CatBoost for big data: an interdisciplinary review

CatBoost for big data: an interdisciplinary review

CatBoost for Big Data: an Interdisciplinary Review

LBPX: A Novel Feature Extraction Method for Iris Recognition

Contact Info

Product

Resources

About