{"title":"Feature Selection Methods on Biological Knowledge Discovery and Data Mining: A Survey","authors":"H. Mhamdi, F. Mhamdi","doi":"10.1109/DEXA.2014.26","DOIUrl":"https://doi.org/10.1109/DEXA.2014.26","url":null,"abstract":"Feature selection is an important component of data mining and knowledge discovery process, due to the availability of data with hundreds of variables leading to data with very high dimension. It aims at reducing the number of features by removing irrelevant or redundant ones, while trying to reduce computation time, preserve or improve prediction performance, and to a better understanding of the data in machine learning or pattern recognition and specific in bioinformatics applications where the number of features is significantly larger than the number of samples. In this paper we provide an overview of some feature selection methods present in literature. We focus on Filter, Wrapper and hybrid methods. We also apply some of the feature selection techniques on standard databank to demonstrate their applicability.","PeriodicalId":291899,"journal":{"name":"2014 25th International Workshop on Database and Expert Systems Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122373899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Power Producers Trading Electricity in Both Pool and Forward Markets","authors":"H. Algarvio, F. Lopes, J. Sousa, J. Lagarto","doi":"10.1109/DEXA.2014.41","DOIUrl":"https://doi.org/10.1109/DEXA.2014.41","url":null,"abstract":"The electricity industry throughout the world, which has long been dominated by vertically integrated utilities, has experienced major changes. Deregulation, unbundling, wholesale and retail wheeling, and real-time pricing were abstract concepts a few years ago. Today market forces drive the price of electricity and reduce the net cost through increased competition. As power markets continue to evolve, there is a growing need for advanced modeling approaches. This article addresses the challenge of maximizing the profit (or return) of power producers through the optimization of their share of customers. Power producers have fixed production marginal costs and decide the quantity of energy to sell in both day-ahead markets and a set of target clients, by negotiating bilateral contracts involving a three-rate tariff. Producers sell energy by considering the prices of a reference week and five different types of clients with specific load profiles. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit.","PeriodicalId":291899,"journal":{"name":"2014 25th International Workshop on Database and Expert Systems Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Formalization of Expert Knowledge: A Disaster Management Case Study","authors":"Mario Pichler, D. Leber","doi":"10.1109/DEXA.2014.42","DOIUrl":"https://doi.org/10.1109/DEXA.2014.42","url":null,"abstract":"Credible computerized approaches to situation assessment for natural disaster management strongly depend on exploitable expert knowledge. A key problem, however, is to find a suitable knowledge representation method that a) is easy to understand and usable by domain experts of different disciplines, and b) is seamlessly usable by computer-based reasoning techniques. Recent logics-based approaches to situation assessment -- ontologies -- suffer from the ability to infer new knowledge that is not based on already known propositions (i.e. abnormal eventualities) and are also difficult to use by non-mathematicians or computer scientists. In this paper, we investigate and introduce a promising approach of formal knowledge representation as core building block of a continuous situation assessment component that explicitly supports inherent characteristics of disaster prevention & management situations. We are modelling networks of influence factors for critical situations, derived from domain experts and historical data, by means of probabilistic graphical models. This kind of models offers a very natural and easy to understand support for domain experts and pays tribute to the major aspects of uncertainty & incompleteness of data in disaster situations.","PeriodicalId":291899,"journal":{"name":"2014 25th International Workshop on Database and Expert Systems Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127870607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions","authors":"F. Fabris, A. Freitas","doi":"10.1109/DEXA.2014.29","DOIUrl":"https://doi.org/10.1109/DEXA.2014.29","url":null,"abstract":"The classification of protein and gene functions is a complex problem that is becoming more relevant as the number of sequenced genes and proteins increases. This work presents a modified version of the Extended Local Hierarchical Naive Bayes algorithm, which exploits the requirements of the original algorithm (single-path, mandatory-leaf-prediction hierarchical classification problems in tree-structured class hierarchies) to greatly improve classification run-time. We show that, considering 18 hierarchical classification datasets, the modified algorithm yields equivalent predictive performance and significantly improves run-time in the training and prediction phases.","PeriodicalId":291899,"journal":{"name":"2014 25th International Workshop on Database and Expert Systems Applications","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116630022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}