A feature selection algorithm for class discrimination improvement

Stefano, Claudio De; Fontanella, Francesco; Marrocco, Claudio; Schirinzi, Gilda

doi:10.1109/igarss.2007.4422821

Cited by 10 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Projectron, much like an MLP, is a supervised learning platform wherein the classification accuracy is dependent on the input features. A higher discrimination of features between each class usually results in a higher classification accuracy [22]. In recent years, several descriptors have been introduced that have shown to complement learning techniques [23] [24]; one such descriptor that has gained traction in the medical imaging domain are Radon projections [7].…”

Section: Projectronmentioning

confidence: 99%

Projectron – A Shallow and Interpretable Network for Classifying Medical Images

Sriram

Kalra

Tizhoosh

2019

2019 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

This paper introduces the "Projectron" as a new neural network architecture that uses Radon projections to both classify and represent medical images. The motivation is to build shallow networks which are more interpretable in the medical imaging domain. Radon transform is an established technique that can reconstruct images from parallel projections. The Projectron first applies global Radon transform to each image using equidistant angles and then feeds these transformations for encoding to a single layer of neurons followed by a layer of suitable kernels to facilitate a linear separation of projections. Finally, the Projectron provides the output of the encoding as an input to two more layers for final classification. We validate the Projectron on five publicly available datasets, a general dataset (namely MNIST) and four medical datasets (namely Emphysema, IDC, IRMA, and Pneumonia). The results are encouraging as we compared the Projectron's performance against MLPs with raw images and Radon projections as inputs, respectively. Experiments clearly demonstrate the potential of the proposed Projectron for representing/classifying medical images.

show abstract

Section: Projectronmentioning

confidence: 99%

Projectron – A Shallow and Interpretable Network for Classifying Medical Images

Sriram

Kalra

Tizhoosh

2019

2019 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

show abstract

“…The feature selection algorithm can be classified into two namely heuristic and metaheuristic approaches. Many heuristic algorithms have www.ijacsa.thesai.org been proposed in the literature for finding near-optimal solutions [76][77].GA is a one of metaheuristic approach and have been widely used to solve feature selection problems [82][83][84][85][86][87]. We have reviewed the introduction, concept and stages in the development of HCR.…”

Section: Current Trend In Feature Selectionmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

View full text Add to dashboard Cite

Abstract-The development of handwriting character recognition (HCR) is an interesting area in pattern recognition.HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

show abstract

“…The effectiveness of the selected features has been tested on a neural network classifier. The obtained results have been compared with those obtained without feature selection and with those obtained by using a previously presented approach [11].…”

Section: Introductionmentioning

confidence: 97%

“…The results of our GA-based method (GA2 in the following) have been compared with those obtained by another GA-based feature algorithm previously proposed in [11] (GA1 in the following), which used a different separability index for feature subset evaluations. That index was computed by using a training set T r, containing C classes, of labelled patterns represented as feature vectors in the initial N -dimensional feature space.…”

Section: Comparison Findingsmentioning

confidence: 99%

A GA-Based Feature Selection Algorithm for Remote Sensing Images

Stefano

Fontanella

Marrocco

2008

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We present a GA-based feature selection algorithm in which feature subsets are evaluated by means of a separability index. This index is based on a filter method, which allows to estimate statistical properties of the data, independently of the classifier used. More specifically, the defined index uses covariance matrices for evaluating how spread out the probability distributions of data are in a given n−dimensional space. The effectiveness of the approach has been tested on two satellite images and the results have been compared with those obtained without feature selection and with those obtained by using a previously developed GAbased feature selection algorithm.

show abstract

A feature selection algorithm for class discrimination improvement

Cited by 10 publications

References 11 publications

Projectron – A Shallow and Interpretable Network for Classifying Medical Images

Projectron – A Shallow and Interpretable Network for Classifying Medical Images

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

A GA-Based Feature Selection Algorithm for Remote Sensing Images

Contact Info

Product

Resources

About