Segmentation and Classification of Skin Lesions for Disease Diagnosis

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.Comment: 10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer Science, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015

A novel termclass relevance measure for text categorization

In this paper, we introduce a new measure called TermClass relevance to compute the relevancy of a term in classifying a document into a particular class. The proposed measure estimates the degree of relevance of a given term, in placing an unlabeled document to be a member of a known class, as a product of ClassTerm weight and ClassTerm density; where the ClassTerm weight is the ratio of the number of documents of the class containing the term to the total number of documents containing the term and the ClassTerm density is the relative density of occurrence of the term in the class to the total occurrence of the term in the entire population. Unlike the other existing term weighting schemes such as TF-IDF and its variants, the proposed relevance measure takes into account the degree of relative participation of the term across all documents of the class to the entire population. To demonstrate the significance of the proposed measure experimentation has been conducted on the 20 Newsgroups dataset. Further, the superiority of the novel measure is brought out through a comparative analysis

An alternative framework for univariate filter based feature selection for text categorization

In this paper, we introduce an alternative framework for selecting a most relevant subset of the original set of features for the purpose of text categorization. Given a feature set and a local feature evaluation function (such as chi-square measure, mutual information etc.,) the proposed framework ranks the features in groups instead of ranking individual features. A group of features with rth rank is more powerful than the group of features with (r+1)th rank. Each group is made up of a subset of features which are supposed to be capable of discriminating every class from every other class. The added advantage of the proposed framework is that it automatically eliminates the redundant features while selecting features without requirement of study of features in combination. Further the proposed framework also helps in handling overlapping classes effectively through selection of low ranked yet powerful features. An extensive experimentation has been conducted on three benchmarking datasets using four different local feature evaluation functions with Support Vector Machine and Naïve Bayes classifiers to bring out the effectiveness of the proposed framework over the respective conventional counterparts

Ensemble of feature selection methods for text classification: An analytical study

In this paper, alternative models for ensembling of feature selection methods for text classification have been studied. An analytical study on three different models with various rank aggregation techniques has been made. The three models proposed for ensembling of feature selection are homogeneous ensemble, heterogeneous ensemble and hybrid ensemble. In homogeneous ensemble, the training feature matrix is randomly partitioned into multiple equal sized training matrices. A common feature evaluation function (FEF) is applied on all the smaller training matrices so as to obtain multiple ranks for each feature. Then a final score for each feature is computed by applying a suitable rank aggregation method. In heterogeneous ensemble, instead of partitioning the training matrix, multiple FEFs are applied onto the same training matrix to obtain multiple rankings for every feature. Then a final score for each feature is computed by applying a suitable rank aggregation method. Hybrid ensembling combines the ranks obtained by multiple homogeneous ensembling through multiple FEFs. It has been experimentally proven on two benchmarking text collections that, in most of the cases the proposed ensembling methods achieve better performance than that of any one of the feature selection methods when applied individually