Faster : A hybrid algorithm for feature selection and record reduction
نویسنده
چکیده
—The amount of data that has to be analysed and processed to assist decision making has significantly increased in recent years. These datasets may contain potentially useful, but as yet undiscovered, information and knowledge. This high dimensionality of datasets leads to the phenomenon known as the curse of dimensionality. When faced with difficulties resulting from the high dimension of a space, the ideal approach is to decrease this dimension, without losing relevant information in the data. The use of Rough-Set theory to achieve feature selection is one approach that has proven successful. However, most approaches carry out reduction in only one dimension i.e in the number of attributes. In this investigation a new algorithm is proposed which allows for record reduction as well as attribute reduction. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER produced an attribute reduction of 30% with a speed improvement of 2.6 times when used as pre-processor for two different rare itemset algorithms.
منابع مشابه
Developing a Filter-Wrapper Feature Selection Method and its Application in Dimension Reduction of Gen Expression
Nowadays, increasing the volume of data and the number of attributes in the dataset has reduced the accuracy of the learning algorithm and the computational complexity. A dimensionality reduction method is a feature selection method, which is done through filtering and wrapping. The wrapper methods are more accurate than filter ones but perform faster and have a less computational burden. With ...
متن کاملDetermining Effective Features for Face Detection Using a Hybrid Feature Approach
Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...
متن کاملImproving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملIFSB-ReliefF: A New Instance and Feature Selection Algorithm Based on ReliefF
Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomp...
متن کاملAn Improved Flower Pollination Algorithm with AdaBoost Algorithm for Feature Selection in Text Documents Classification
In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature spa...
متن کامل