ANOVA-based feature analysis and selection in HMM-based offline signature verification system

Blumenstein

Proceedings of the International Conference &Amp; Workshop on Emerging Trends in Technology - ICWET '11

2011

Signatures are extensively used as a means of personal verification. Manual signature-based authentication of a large number of documents is a very difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable physical unique characteristics (hand geometry, iris scan, finger prints or DNA) or behavioural features. Human signatures provide secure means for confirmation and authorization in legal documents. So nowadays, automatic signature verification becomes an essential component. In order to convey the stateof-the-art in the field to researchers, in this paper we present a survey of off-line signature verification systems.

Section: Different Methods For Off-line Signature Verification Systemsmentioning

confidence: 99%

Off-line signature verification systems

Blumenstein

Proceedings of the International Conference &Amp; Workshop on Emerging Trends in Technology - ICWET '11

2011

Computational and Mathematical Methods in Medicine

“…In order to select the optimal features, the combination of the IFS method and different feature selection methods will be introduced. Meanwhile, in order to clearly reflect the superiority of the LightGBM feature selection method, we also use ReliefF [ 50 ], LinearSVR [ 51 ], XGBoost [ 52 ], and ANOVA [ 53 ] to find the optimal feature subset.…”

Section: Resultsmentioning

confidence: 99%

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model

Zhang

Liu

Qin

et al. 2020

Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k -spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F -measure.

“…Many studies have focused on statistical features like image statistics, analysis of variance [16,17], and mean and median [18]. Handwriting feature like position, velocity, acceleration, and pressure were analyzed in Reference [17].…”

Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.