Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task

This work is builds on the study of the 10 top data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) community in December 2006. We address the same study, but with the application of statistical tests to establish, a more appropriate and justified ranking classifier for classification tasks. Current studies and practices on theoretical and empirical comparison of several methods, approaches, advocated tests that are more appropriate. Thereby, recent studies recommend a set of simple and robust non-parametric tests for statistical comparisons classifiers. In this paper, we propose to perform non-parametric statistical tests by the Friedman test with post-hoc tests corresponding to the comparison of several classifiers on multiple data sets. The tests provide a better judge for the relevance of these algorithms

Brain Structures Segmentation by using Statistical Models

Background: Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The goal of our work is to implement an automatic brain’s structures segmentation method, to evaluate its use in computer aided diagnosis tools, and to compare their performances. Methods: The proposed method consists of the segmentation of brain’s structures that uses the active shape models (ASM) and active appearance models (AAM) techniques. Results: The experimental results demonstrate the superiority of method AAM over the other method ASM. Conclusion: In this paper, we have evaluated and compared two methods using several comparison criteria, to identify the best one. After several performance measures, we can conclude that the AAM is better than the AS

A NEURO-FUZZY INFERENCE MODEL FOR BREAST CANCER RECOGNITION

ABSTRACT Breast cancer is known as one of the most common cancers to afflict the female population. Computer assisted diagnosis can be helpful for doctors in detection and diagnosing of potential abnormalities. Several techniques can be useful for accomplishing this task. This paper outlines an approach for recognizing breast cancer diagnosis using neuro-fuzzy inference technique namely ANFIS (Adaptative Neuro-Fuzzy Inference System). Wisconsin breast cancer diagnosis (WBCD)database developed at University of California, Irvine (UCI) is used to evaluate this method. Results show that the best performances are obtained by our model compared to others cited in literatur (an accuracy of 98, 25 % )

Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning

Computer aided detection and diagnosis systems based on deep learning have shown promising performance in breast cancer detection. However, there are cases where the obtained results lack justification. In this study, our objective is to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as benign or malignant. We compare these regions with the regions identified by pathologists. To achieve this, we employed the VGG19 architecture and tested three visualization methods: Gradient, LRP Z, and LRP Epsilon. Additionally, we experimented with three pixel selection methods: Bins, K-means, and MeanShift. Based on the results obtained, the Gradient visualization method and the MeanShift selection method yielded satisfactory outcomes for visualizing the images

Recherche des Services Web en Utilisant le Contenu d’OWLS

L’objectif de cet article est de résoudre le problème de matching (recherche) des services web. Dans ce cadre nous présentons une approche basée sur le contenu des documents OWLS. Tout d’abord nous supposons que l’ensemble des services web de la collection de test est segmenté en sept classes. Apres nous représentons chaque service, par un vecteur de fréquences des termes, ceci peut être fait en exploitant les descriptions informelles (en langage naturel) contenues dans les documents OWLS, après nous exploitons, ces vecteurs de fréquences et les concepts d’entrées-sorties associés aux couples (requête, service web), ainsi que de la mesure de similarité cosine, pour faire l’appariement (matching). Les résultats obtenus ont été largement acceptables, et encouragent les travaux de recherche sur cette piste.Mots clés : Recherche de services web, Mesures de similarité, ontologies, Owls, text minin

Reinforced confidence in self-training for a semi-supervised medical data classification

International audienc