A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

AbstractIn any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

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FUZZY UNORDERED RULE USING GREEDY HILL CLIMBING FEATURE SELECTION METHOD: AN APPLICATION TO DIABETES CLASSIFICATION

Journal of Information and Communication Technology ◽

10.32890/jict2021.20.3.5 ◽

2021 ◽

Vol 20 (Number 3) ◽

pp. 391-422

Author(s):

Hayder Naser Khraibet Al-Behadili ◽

Ku Ruhana Ku-Mahamud

Keyword(s):

Fuzzy Logic ◽

Feature Selection ◽

Feature Selection Method ◽

Classification Problem ◽

Classification Criteria ◽

Hill Climbing ◽

Classification Model ◽

Support Vector ◽

Input Variables ◽

Fuzzy Logic Technique

Diabetes classification is one of the most crucial applications of healthcare diagnosis. Even though various studies have been conducted in this application, the classification problem remains challenging. Fuzzy logic techniques have recently obtained impressive achievements in different application domains especially medical diagnosis. Fuzzy logic technique is not able to deal with data of a large number of input variables in constructing a classification model. In this research, a fuzzy logic technique using greedy hill climbing feature selection methods was proposed for the classification of diabetes. A dataset of 520 patients from the Hospital of Sylhet in Bangladesh was used to train and evaluate the proposed classifier. Six classification criteria were considered to authenticate the results of the proposed classifier. Comparative analysis proved the effectiveness of the proposed classifier against Naive Bayes, support vector machine, K-nearest neighbour, decision tree, and multilayer perceptron neural network classifiers. Results of the proposed classifier demonstrated the potential of fuzzy logic in analyzing diabetes patterns in all classification criteria.

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A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2012010102 ◽

2012 ◽

Vol 2 (1) ◽

pp. 16-41 ◽

Cited By ~ 1

Author(s):

Htwe Pa Pa Win ◽

Phyo Thu Thu Khine ◽

Khin Nwe Ni Tun

Keyword(s):

Feature Extraction ◽

Structural Analysis ◽

Character Recognition ◽

Optical Character Recognition ◽

Extraction Method ◽

Recognition Performance ◽

Extraction Methods ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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PROTEIN FOLD CLASSIFICATION WITH GENETIC ALGORITHMS AND FEATURE SELECTION

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004321 ◽

2009 ◽

Vol 07 (05) ◽

pp. 773-788 ◽

Cited By ~ 2

Author(s):

PENG CHEN ◽

CHUNMEI LIU ◽

LEGAND BURGE ◽

MOHAMMAD MAHMOOD ◽

WILLIAM SOUTHERLAND ◽

...

Keyword(s):

Support Vector Machine ◽

Genetic Algorithms ◽

Feature Selection ◽

Training Dataset ◽

Support Vector ◽

Protein Fold ◽

Classification Rate ◽

Test Dataset ◽

Feature Vectors ◽

Protein Folds

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

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A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026819500202 ◽

2019 ◽

Vol 18 (03) ◽

pp. 1950020 ◽

Cited By ~ 13

Author(s):

Alok Kumar Shukla ◽

Pradeep Singh ◽

Manu Vardhan

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Classification Accuracy ◽

B Cell Lymphoma ◽

Feature Subset Selection ◽

Classification Model ◽

Significant Feature ◽

Support Vector ◽

Feature Subset ◽

Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

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Hindi Text Document Classification System Using SVM and Fuzzy

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2018100101 ◽

2018 ◽

Vol 5 (4) ◽

pp. 1-31 ◽

Cited By ~ 8

Author(s):

Shalini Puri ◽

Satya Prakash Singh

Keyword(s):

Classification System ◽

Character Recognition ◽

Optical Character Recognition ◽

Document Classification ◽

Data Availability ◽

Support Vector ◽

Handwritten Documents ◽

Text Document ◽

Survey Report ◽

Text Document Classification

In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

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Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

The Aeronautical Journal ◽

10.1017/aer.2019.72 ◽

2019 ◽

Vol 123 (1267) ◽

pp. 1415-1436 ◽

Cited By ~ 1

Author(s):

A. B. A. Anderson ◽

A. J. Sanjeev Kumar ◽

A. B. Arockia Christopher

Keyword(s):

Data Mining ◽

Feature Selection ◽

Classification Model ◽

System Level ◽

Support Vector ◽

Flight Delays ◽

Data Mining Techniques ◽

Mining Methods ◽

Artificial Neural Network Ann ◽

Aircraft System

ABSTRACTData mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

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Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽

10.3390/molecules25061442 ◽

2020 ◽

Vol 25 (6) ◽

pp. 1442 ◽

Cited By ~ 2

Author(s):

Tao Shen ◽

Hong Yu ◽

Yuan-Zhong Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Related Species ◽

Predictive Accuracy ◽

Classification Model ◽

Venn Diagram ◽

Support Vector ◽

Stacked Generalization ◽

Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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Email Worm Detection Using Data Mining

Techniques and Applications for Advanced Information Privacy and Security ◽

10.4018/978-1-60566-210-7.ch002 ◽

2011 ◽

pp. 20-34

Author(s):

Mohammad M. Masud ◽

Latifur Khan ◽

Bhavani Thuraisingham

Keyword(s):

Data Mining ◽

Feature Selection ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Two Phase ◽

Feature Selection Technique ◽

Worm Detection ◽

Phase Selection ◽

Using Data

This chapter applies data mining techniques to detect email worms. Email messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of features is reduced using two different approaches: feature-selection and dimension-reduction. This step is necessary to reduce noise and redundancy from the data. The feature-selection technique is called Two-phase Selection (TPS), which is a novel combination of decision tree and greedy selection algorithm. The dimensionreduction is performed by Principal Component Analysis. Second, the reduced data is used to train a classifier. Different classification techniques have been used, such as Support Vector Machine (SVM), Naïve Bayes and their combination. Finally, the trained classifiers are tested on a dataset containing both known and unknown types of worms. These results have been compared with published results. It is found that the proposed TPS selection along with SVM classification achieves the best accuracy in detecting both known and unknown types of worms.

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