Results 1 - 10 of 180

Improving feature selection techniques for machine learning

by Feng Tan, Feng Tan, Under Direction, Anu G. Bourgeois - Adviser-Bourgeois, Anu G , 2007
"... As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by le ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built

An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset

by Senthilkumar Devaraj , S Paulraj
"... Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier bu ..."
Abstract - Add to MetaCart
built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset of the features of the MDD for further analysis or to design a classifier. In this paper, an efficient feature selection algorithm

Feature selection using integer and binary . . .

by D. Nithya , V. Suganya , R. Saranya Irudaya Mary , 2013
"... This paper presents, a Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier. Data Mining (DM) is the process of exploration and analysis, by automatic or semiautomatic means of large quantities of data in order to discover meaningful pattern ..."
Abstract - Add to MetaCart
patterns and rules. DM methods can be divided into supervised and unsupervised learning techniques. Classification is a supervised learning technique. In this paper classification algorithms like Support Vector Machine (SVM) and Genetic Algorithm (GA) are used to find the classification accuracy

MACHINE

by Feng Tan, Under Direction, Anu G. Bourgeois, Feng Tan , 2007
"... As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by le ..."
Abstract - Add to MetaCart
As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built

Feature Subset Selection for Rule Induction Using RIPPER

by Jihoon Yang, Asok Tiyyagura, Fajun Chen, Vasant Honavar
"... The choice of features or attributes used to represent patterns in the synthesis of pattern classifiers using machine learning algorithms has a strong impact on the accuracy of the classifier, the number of examples needed to attain a given classification accuracy on test data, the cost of classific ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
and the cost of pattern classification. Evolutionary algorithms, because of their ability to find good solutions offer a promising approach to such a multicriteria optimization problem. Results of experiments reported in this paper demonstrate that feature subset selection using a genetic algorithm results

Research Article An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset

by Senthilkumar Devaraj, S. Paulraj
"... Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multidimensional medical data classification has recently received increased attention by researchers working onmachine learning and data mining. In multid ..."
Abstract - Add to MetaCart
. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset

Feature Selection using Multi-objective Genetic Algorithm: A Hybrid Approach

by Jyoti Ahuja, Saroj Dahiya Ratnoo
"... Abstract. Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search ..."
Abstract - Add to MetaCart
Abstract. Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large

Evolutionary search of thresholds for robust feature set selection: application to the analysis of microarray data

by Carlos Cotta, Christian Sloper, Pablo Moscato - in: Proceedings of EvoBio2004 - 2 nd European Workshop on Evolutionary Computation and Bioinformatics , 2004
"... Abstract. We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techniques to preprocess the labeled datasets, these methods are generally not linked with the combinatorial properties of th ..."
Abstract - Cited by 10 (10 self) - Add to MetaCart
of the final solutions. We prove that it is NP −hard to obtain an appropriate set of thresholds that will transform a given dataset into a binary instance of a robust feature subset selection problem. We address this problem using an evolutionary algorithm that learns the appropriate value of the thresholds

Feature Selection by Means of a Feature Weighting Approach

by M. Scherf, W. Brauer - Forschungsberichte Kunstliche Intelligenz, Institut fur Informatik, Technische Universitat Munchen , 1997
"... . Selecting a set of features which is optimal for a given classification task is one of the central problems in machine learning. We address the problem using the flexible and robust filter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and ..."
Abstract - Cited by 18 (1 self) - Add to MetaCart
. Selecting a set of features which is optimal for a given classification task is one of the central problems in machine learning. We address the problem using the flexible and robust filter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights

Application of Feature Subset Selection based on Evolutionary Algorithms for Automatic Emotion Recognition in Speech

by Aitor Álvarez, Idoia Cearreta, Juan Miguel López, Andoni Arruti, Elena Lazkano, Basilio Sierra, Nestor Garay
"... The study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. In this paper we present a study performed to analyze different m ..."
Abstract - Add to MetaCart
recognition. In this particular case, techniques based on evolutive algorithms (EDA) have been used to select speech feature subsets that optimize automatic emotion recognition success rate. Achieved experimental results show a representative increase in the abovementioned success rate. 1.
Results 1 - 10 of 180