E. Jebamalar Leavline

Bio: E. Jebamalar Leavline is an academic researcher from Anna University. The author has contributed to research in topics: Feature selection & Dimensionality reduction. The author has an hindex of 8, co-authored 28 publications receiving 241 citations. Previous affiliations of E. Jebamalar Leavline include Bharathidasan Institute of Technology.

Literature Review on Feature Selection Methods for High-Dimensional Data

Abstract: selection is a process of removing the redundant and the irrelevant features from a dataset to improve the performance of the machine learning algorithms. The feature selection is also known as variable selection or attribute selection. The features are also known as variables or attributes. The machine learning algorithms can be roughly classified into two categories one is supervised learning algorithm and another one is unsupervised learning algorithm. The supervised learning algorithms learn the labeled data and construct learning models that are known as classifiers. The classifiers are employed for classification or prediction to identify or predict the class-label of the unlabeled data. The unsupervised learning algorithms lean the unlabeled data and construct the learning models that known as clustering models. The clustering models are employed to cluster or categorize the given data for predicting or identifying their group or cluster. Mostly, the feature selections are employed for the supervised learning algorithms since they suffered by the high-dimensional space. Therefore, this paper presents a complete literature review on various feature selection methods for high-dimensional data.

Dimensionality Reduction using Genetic Algorithm for Improving Accuracy in Medical Diagnosis

Abstract: The technological growth generates the massive data in all the fields. Classifying these highdimensional data is a challenging task among the researchers. The high-dimensionality is reduced by a technique is known as attribute reduction or feature selection. This paper proposes a genetic algorithm (GA)based features selection to improve the accuracy of medical data classification. The main purpose of the proposed method is to select the significant feature subset which gives the higher classification accuracy with the different classifiers. The proposed genetic algorithmbased feature selection removes the irrelevant features and selects the relevant features from original dataset in order to improve the performance of the classifiers in terms of time to build the model, reduced dimension and increased accuracy. The proposed method is implemented using MATLAB and tested using the medical dataset with various classifiers namely Naive Bayes, J48, and kNN and it is evident that the proposed method outperforms other methods compared.

Salt and Pepper Noise Detection and Removal in Gray Scale Images: An Experimental Analysis

Abstract: Impulse noise removal is a mechanism for detection and removal of impulse noise from images. Median filters are preferred for removing impulse noise because of their simplicity and less computational complexity. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index.

Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

Abstract: In the digital era, cloud computing plays a significant role in scalable resource sharing to carry out seamless computing and information sharing. Securing the data, resources, applications and infrastructure of the cloud is a challenging task among the researchers. To secure the cloud, cloud security controls are deployed in the cloud computing environment. The cloud security controls are roughly classified as deterrent controls, preventive controls, detective controls and corrective controls. Among these, detective controls are significantly contributing for cloud security by detecting the possible intrusions to prevent the cloud environment from the possible attacks. This detective control mechanism is established using intrusion detection system (IDS). The detecting accuracy of the IDS greatly depends on the network traffic data that is employed to develop the IDS using machine-learning algorithm. Hence, this paper proposed a cuckoo optimisation-based method to preprocess the network traffic data for improving the detection accuracy of the IDS for cloud security. The performance of the proposed algorithm is compared with the existing algorithms, and it is identified that the proposed algorithm performs better than the other algorithms compared.

Gaussian noise removal in gray scale images using fast Multiscale Directional Filter Banks

Abstract: This paper presents a novel approach for Gaussian noise removal using Multiscale Filter Banks for the Contourlet Transform. The Multiscale Directional Filter Bank (MDFB) improves the radial frequency resolution of the Contourlet Transform by introducing an additional decomposition in the high frequency band. This reduces the computational complexity significantly by saving a directional decomposition because of the change in the order of decomposition. Scaling is performed by a low pass filtering based splitting and the scale decomposition is done by the Directional Filter Bank. Perfect reconstruction is possible for the scale decomposition regardless of the choice of the low pass filter. MDFB outperforms the conventional Wavelet and Contourlet transform methods for Gaussian noise removal. Denoising performance of this proposed method is compared with Wavelet and Contourlet based denoising schemes with state of art threshold methods.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Communications of the ACM

Abstract: Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Binary grasshopper optimisation algorithm approaches for feature selection problems

Abstract: Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature.