Feature Selection by Combining Multiple Methods

2007 IEEE Workshop on Automatic Identification Advanced Technologies, 2007

Two different Genetic Algorithm (GA) architectures are applied to a feature selection problem in on-line signature verification. The standard GA with binary coding is first used to find a suboptimal subset of features that minimizes the verification error rate of the system. The curse of dimensionality phenomenon is further investigated using a GA with integer coding. Results are given on the MCYT signature database comprising 330 users (16500 signatures). Signatures are represented by means of a set of 100 features which can be divided into four different groups according to the signature information they contain, namely: i) time, ii) speed and acceleration, iii) direction, and iv) geometry. The GA indicates that features from subsets i and iv are the most discriminative when dealing with random forgeries, while parameters from subsets ii and iv are the most appropriate to maximize the recognition rate with skilled forgeries.

Intelligent Data Analysis, 1997

Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970's to the present. It identifies four steps of a typical feature selection method, and categorizes the different existing methods in terms of generation procedures and evaluation functions, and reveals hitherto unattempted combinations of generation procedures and evaluation functions. Representative methods are chosen from each category for detailed explanation and discussion via example. Benchmark datasets with different characteristics are used for comparative study. The strengths and weaknesses of different methods are explained. Guidelines for applying feature selection methods are given based on data types and domain characteristics. This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.

Optimization Methods and Software, 2007

Feature selection (FS) is a major issue in developing efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of this paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of important issues with respect to the functionality of FSAs, such as: (a) their ability to identify relevant features, (b) the performance of the classification models developed on a reduced set of features, (c) the reduction in the number of features, and (d) the interactions between different FSAs with the techniques used to develop a classification model. The analysis considers a variety of FSAs and classification methods.

Webology, 2021

The eradication of correlated evidence of the enormous volume of the directory is designated as data mining. Extracting discriminate knowledge associate with the approach is performed by a feature of knowledge. Knowledge rejuvenation is carried out as features and the process is delineated as a feature selection mechanism. Feature selection is a subset of features, acquired more information. Before data mining, Feature selection is essential to trim down the elevated dimensional information. Without feature selection pre-processing techniques, classification required interminable calculation duration which might lead to intricacy. The foremost intention of the analysis is to afford a summary of feature selection approaches adopted to evaluate the extreme extensive features.

Pattern recognition is a vast field which has seen significant advances over the years. As the datasets under consideration grow larger and more comprehensive, using efficient techniques to process them becomes increasingly important. We present a versatile technique for the purpose of feature selection and extraction -Class Dependent Features (CDFs). CDFs identify the features innate to a class and extract them accordingly. The features thus extracted are relevant to the entire class and not just to the individual data item. This paper focuses on using CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to two problem statements which have very little in common with each other -handwritten digit recognition and text categorization. It is found that for both problem statements, the accuracy is comparable to state-of-the-art results and the speed of the operation is considerably greater. Results are presented for Reuters-21578 and Web-KB datasets relating to text categorization and the MNIST and USPS datasets for handwritten digit recognition.

International journal of computing and digital system/International Journal of Computing and Digital Systems, 2024

In the recent digital age, online signature verification plays a crucial role in authentication, including security standards across many industries, such as financial, legal, and e-commerce. Bank'srld Bank's data shows the global digital economy is growing fast, with internet usage for nearly 60% of people worldwide. According to numbers from the International Telecommunications Union, over 4.7 billion individuals have become internet users. With so much internet online, security and trust for online transactions are essential issues. Forensics and biometrics are emerging as key players in this area. Verifying signatures digitally is one important use. As in the study mentioned earlier, using machine learning can help make signature verification systems more accurate and reliable. Our study describes an online verification method using machine learning based on a signature's dynamic features and compares the outcomes to methods already in use. The online signature verification has been validated using supervised learning (K-nearest neighbor (KNN)). This research aimed to enhance authenticity and reduce the occurrence of false positives as its primary objectives. The outcomes show that this methodology has better authenticity than the current methods. The Signature Verification System (SVS) 2004-based signature datasets are utilized in the tests.

Pattern Recognition, 2000

A comparative study of algorithms for large-scale feature selection (where the number of features is over 50) is carried out. In the study, the goodness of a feature subset is measured by leave-one-out correct-classi cation rate of a nearest-neighbor (1-NN) classi er and many practical problems are used. A uni ed way is given to compare algorithms having dissimilar objectives. Based on results of many experiments, we give guidelines for the use of feature selection algorithms. Especially, it is shown that sequential oating search methods are suitable for small-and mediumscale problems and genetic algorithms are suitable for large-scale problems.

Asian Journal of Computer Science and Technology, 2018

Feature selection has been developed by several mining techniques for classification. Some existing approaches couldn’t remove the irrelevant data from dataset for class. Thus it needs the selection of appropriate features that emphasize its role in classification. For this it consider the statistical method like correlation coefficient to identify the features from feature set whose data are very important for existing classes. The several methods such as Gaussian process, linear regression and Euclidean distance have taken into consideration for clarity of classification. The experimental results reveal that the proposed method identifies the exact relevant features for several classes.

2008

Abstract The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and the resulting learner. For this reason, many methods of automatic feature selection have been developed. By using the modularization of feature selection process, this paper evaluates a wide spectrum of these methods and some additional ones created by combination of different search and measure modules.

International Journal of Computer Applications, 2014

The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classification algorithm produces the result as binary class. When there is a multiclass dataset, the classification algorithm reduces the dataset into a binary class for simplification purpose by using any one of the data reduction methods and the algorithm is applied for prediction. When data reduction on original dataset is carried out, the quality of the data may degrade and the accuracy of an algorithm will get affected. To maintain the effectiveness of the data, the multiclass data must be treated with its original form without maximum reduction, and the algorithm can be applied on the dataset for producing maximum accuracy. Dataset with maximum number of attributes like thousands must incorporate the best feature selection algorithm for selecting the relevant features to reduce the space and time complexity. The performance of Classification algorithm is estimated by how accurately it predicts the individual class on particular dataset. The accuracy constrain mainly depends on the selection of appropriate features from the original dataset. The feature selection algorithms play an important role in classification for better performance. The feature selection is one of the preprocessing techniques in the classification. This research paper deals with different feature selection algorithms and their performance on different dataset.