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The data mining applications such as bioinformatics, risk management, forensics etc., involves very high dimensional dataset. Due to large number of dimensions, a well known problem of “Curse of Dimensionality” occurs. This problem leads to lower accuracy of machine learning classifiers due to involvement of many insignificant and irrelevant dimensions or features in the dataset. There are many methodologies...
In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the generalization performance for classification problems. In this system, IGSA is designed by combining the search strategy of particle swarm optimization and GSA to effectively reduce the problem...
The purpose of the feature selection is to eliminate insignificant features from entire dataset and simultaneously to keep the class discriminatory information for classification problems. Many feature selection algorithms have been proposed to measure the relevance and redundancy of the features and class variables. In this paper, we proposed an improved feature selection algorithm based on maximum...
K nearest neighbor algorithm (K-NN) is considered as one of the machine learning algorithms for data classification. This algorithm suffers of some disadvantages such as sensitivity to the distance function, K value selection and high computational complexity (time and spatial). In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting with k value...
Feature selection is an indispensable preprocessing step for effective analysis of high dimensional data. In this paper a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model. In this graph, each feature has two nodes, one for selecting that feature and the other for...
Data reduction is an important pre-processing step to both supervised and unsupervised machine learning problems. In this paper, we investigate, in a first part, the two existing strategies for data reduction which are feature selection (FS) and dimensionality reduction (DR). In a second part, we study the impact of different data reduction methods on supervised machine learning in terms of classification...
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At...
Intrusion detection is a critical component of secure information systems. Data Intrusion Detection Processing System often contains a lot of redundancy and noise features, bringing the system a large amount of computing resources, a long training time, a poor real-time, and a bad detection rate. For high dimensional data, feature selection can find the information-rich feature subset, thus enhance...
This paper presents a novel feature selection algorithm based on the technique of mining association rules. The main idea of the proposed algorithm is to find the features that are closely correlative with the class attribute by association rules mining method. Experimental results on several real and artificial data sets demonstrate that the proposed feature selection algorithm is able to obtain...
As important machine learning problems, feature and instance selection have faced relevant improvements in the quality of the algorithms that solve them individually. However, little work has been done to implement ways to solve them simultaneously. In this paper, we introduce an algorithm that combines solutions for both problems, using a simple adaptation of the simulated annealing metaheuristic...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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