One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

A Survey on Soft Subspace Clustering

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been extensively studied and well accepted by the scientific community, SSC algorithms are relatively new but gaining more attention in recent years due to better adaptability. In the paper, a comprehensive survey on existing SSC algorithms and the recent development are presented. The SSC algorithms are classified systematically into three main categories, namely, conventional SSC (CSSC), independent SSC (ISSC) and extended SSC (XSSC). The characteristics of these algorithms are highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201

MapReduce based Classification for Microarray data using Parallel Genetic Algorithm

Inorder to uncover thousands of genes Microarray   produces high throughput is used. Only few gene expression data out of thousands of data is used for disease predication and also for disease classification in medical environment.  To find such initial coexpressed gene groups of clusters whose joint expression is strongly related with the class label A Supervised attribute clustering is used. By sharing the information between each attributes the Mutual Information uses the information of sample varieties to measure the similarity among the attributes. From this the redundant and irrelevant attributes are removed. After forming the clusters the PGA is used to find the optimal feature and is given as mapper function so as to improve the class separability. Using this method the diagnosis can be made easier and effective since its done parallelly. The predictive accuracy is estimated using all the three classifiers such as K-nearest neighbours including naive bayes and Support Vector machine. Thus the overall approach used reducer function which provides excellent predictive capability for accurate medical diagnosis