Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms.  A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible

Improving the classification performance on imbalanced data sets via new hybrid parameterisation model

The aim of this work is to analyse the performance of the new proposed hybrid parameterisation model in handling problematic data. Three types of problematic data will be highlighted in this paper: i) big data set, ii) uncertain and inconsistent data set and iii) imbalanced data set. The proposed hybrid model is an integration of three main phases which consist of the data decomposition, parameter reduction and parameter selection phases. Three main methods, which are soft set and rough set theories, were implemented to reduce and to select the optimised parameter set, while a neural network was used to classify the optimised data set. This proposed model can process a data set that might contain uncertain, inconsistent and imbalanced data. Therefore, one additional phase, data decomposition, was introduced and executed after the pre-processing task was completed in order to manage the big data issue. Imbalanced data sets were used to evaluate the capability of the proposed hybrid model in handling problematic data. The experimental results demonstrate that the proposed hybrid model has the potential to be implemented with any type of data set in a classification task, especially with complex data sets

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous Computing Environments

Multistage feature selection methods for data classification

In data analysis process, a good decision can be made with the assistance of several sub-processes and methods. The most common processes are feature selection and classification processes. Various methods and processes have been proposed to solve many issues such as low classification accuracy, and long processing time faced by the decision-makers. The analysis process becomes more complicated especially when dealing with complex datasets that consist of large and problematic datasets. One of the solutions that can be used is by employing an effective feature selection method to reduce the data processing time, decrease the used memory space, and increase the accuracy of decisions. However, not all the existing methods are capable of dealing with these issues. The aim of this research was to assist the classifier in giving a better performance when dealing with problematic datasets by generating optimised attribute set. The proposed method comprised two stages of feature selection processes, that employed correlation-based feature selection method using a best first search algorithm (CFS-BFS) and as well as a soft set and rough set parameter selection method (SSRS). CFS-BFS is used to eliminate uncorrelated attributes in a dataset meanwhile SSRS was utilized to manage any problematic values such as uncertainty in a dataset. Several bench-marking feature selection methods such as classifier subset evaluation (CSE) and principle component analysis (PCA) and different classifiers such as support vector machine (SVM) and neural network (NN) were used to validate the obtained results. ANOVA and T-test were also conducted to verify the obtained results. The obtained averages for two experimentalworks have proven that the proposed method equally matched the performance of other benchmarking methods in terms of assisting the classifier in achieving high classification performance for complex datasets. The obtained average for another experimental work has shown that the proposed work has outperformed the other benchmarking methods. In conclusion, the proposed method is significant to be used as an alternative feature selection method and able to assist the classifiers in achieving better accuracy in the classification process especially when dealing with problematic datasets

Improved scheme of e-mail spam classification using meta-heuristics feature selection and support vector machine

With the technological revolution in the 21st century, time and distance of communication are decreased by using electronic mail (e-mail). Furthermore, the growing use of e-mail has led to the emergence and further growth problems caused by unsolicited bulk e-mails, commonly referred to as spam e-mail. Many of the existing supervised algorithms like the Support Vector Machine (SVM) were developed to stop the spam e-mail. However, the problem of dealing with large data and high dimensionality of feature space can lead to high execution-time and low accuracy of spam e-mail classification. Nowadays, removing the irrelevant and redundant features beside finding the optimal (or near-optimal) subset of features significantly influences the performance of spam e-mail classification; this has become one of the important challenges. Therefore, in order to optimize spam e-mail classification accuracy, dimensional reduction issues need to be solved. Feature selection schemes become very important in order to reduce the dimensionality through selecting a proper subset feature to facilitate the classification process. The objective of this study is to investigate and improve schemes to reduce the execution time and increase the accuracy of spam e-mail classification. The methodology of this study comprises of four schemes: one-way ANOVA f-test, Binary Differential Evolution (BDE), Opposition Differential Evolution (ODE) and Opposition Particle Swarm Optimization (OPSO), and combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The four schemes were used to improve the spam e-mail classification accuracy. The classification accuracy of the proposed schemes were 95.05% with population size of 50 and 1000 number of iterations in 20 runs and 41 features. The experiment of the proposed schemes were carried out using spambase and spamassassin benchmark dataset to evaluate the feasibility of proposed schemes. The experimental findings demonstrate that the improved schemes were able to efficiently reduce the number of features as well as improving the e-mail classification accuracy

A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means

Recent advances in information technology have led to significant changes in today‟s world. The generating and collecting data have been increasing rapidly. Popular use of the World Wide Web (www) as a global information system led to a tremendous amount of information, and this can be in the form of text document. This explosive growth has generated an urgent need for new techniques and automated tools that can assist us in transforming the data into more useful information and knowledge. Data mining was born for these requirements. One of the essential processes contained in the data mining is classification, which can be used to classify such text documents and utilize it in many daily useful applications. There are many classification methods, such as Bayesian, K-Nearest Neighbor, Rocchio, SVM classifier, and Soft Set Theory used to classify text document. Although those methods are quite successful, but accuracy and efficiency are still outstanding for text classification problem. This study is to propose a new approach on classification problem based on hybrid fuzzy soft set theory and supervised fuzzy c-means. It is called Hybrid Fuzzy Classifier (HFC). The HFC used the fuzzy soft set as data representation and then using the supervised fuzzy c-mean as classifier. To evaluate the performance of HFC, two well-known datasets are used i.e., 20 Newsgroups and Reuters-21578, and compared it with the performance of classic fuzzy soft set classifiers and classic text classifiers. The results show that the HFC outperforms up to 50.42% better as compared to classic fuzzy soft set classifier and up to 0.50% better as compare classic text classifier

Three dimensional finite element modeling, when drilling of Ti-6Al-4V

Finite element modeling (FEM) is widely used to optimize machining processes, to predict and analyze the cutting force, cutting temperature and other related responses. Most of the FEM studies were conducted under the two dimensional orthogonal cutting. Drilling process, which involves oblique cutting is not suitable for orthogonal cutting modelling. Therefore, an attempt to simulate a three dimensional simulation of the drilling process is required. A commercially available software called DEFORM is used to accomplish the task. The value of thrust force from the simulation is compared with the experimental results and they are both in a good agreement. Comparison of the drill temperature at TC1 and TC2 are within an error margin of 12%