Sequential multi-criteria feature selection algorithm based on agent genetic algorithm

Li, Yongming; Zeng, Xiaoping

doi:10.1007/s10489-008-0153-8

Cited by 18 publications

(11 citation statements)

References 32 publications

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“…For our future work, we are going to integrate our selection criterion into more advanced searching strategies such as branch and bound [21], genetic search [3,20,22]. Another problem that should be considered is the quantization process.…”

Section: Resultsmentioning

confidence: 99%

A novel feature selection method based on normalized mutual information

2011

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In this paper, a novel feature selection method based on the normalization of the well-known mutual information measurement is presented. Our method is derived from an existing approach, the max-relevance and minredundancy (mRMR) approach. We, however, propose to normalize the mutual information used in the method so that the domination of the relevance or of the redundancy can be eliminated. We borrow some commonly used recognition models including Support Vector Machine (SVM), k-Nearest-Neighbor (kNN), and Linear Discriminant Analysis (LDA) to compare our algorithm with the original (mRMR) and a recently improved version of the mRMR, the Normalized Mutual Information Feature Selection (NMIFS) algorithm. To avoid data-specific statements, we conduct our classification experiments using various datasets from the UCI machine learning repository. The results confirm that our feature selection method is more robust than the others with regard to classification accuracy.

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Section: Resultsmentioning

confidence: 99%

A novel feature selection method based on normalized mutual information

2011

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“…A genetic algorithm is a search algorithm for optimization problems. The core theory behind genetic algorithms is to base the algorithm on observed traits from evolutionary biology, including inheritance, mutation, selection, and crossover [39]. The steps of the genetic algorithm are:…”

Section: Features Extraction and Eeg Classificationmentioning

confidence: 99%

Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves

Liu

Chiang

Hsu

2013

Sensors

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Driving safety has become a global topic of discussion with the recent development of the Smart Car concept. Many of the current car safety monitoring systems are based on image discrimination techniques, such as sensing the vehicle drifting from the main road, or changes in the driver's facial expressions. However, these techniques are either too simplistic or have a low success rate as image processing is easily affected by external factors, such as weather and illumination. We developed a drowsiness detection mechanism based on an electroencephalogram (EEG) reading collected from the driver with an off-the-shelf mobile sensor. This sensor employs wireless transmission technology and is suitable for wear by the driver of a vehicle. The following classification techniques were incorporated: Artificial Neural Networks, Support Vector Machine, and k Nearest Neighbor. These classifiers were integrated with integration functions after a genetic algorithm was first used to adjust the weighting for each classifier in the integration function. In addition, since past studies have shown effects of music on a person's state-of-mind, we propose a personalized music recommendation mechanism as a part of our system. Through the in-car stereo system, this music recommendation mechanism can help prevent a driver from becoming drowsy due to monotonous road conditions. Experimental results demonstrate the effectiveness of our proposed drowsiness detection method to determine a driver's state of mind, and the music recommendation system is therefore able to reduce drowsiness.

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“…All these methods search for optimal or near optimal subsets of features that optimize a given criterion. Genetic algorithm (GA) has been utilized to realize dimensionality reduction [3][4][5]. In GA, a set of solutions instead of a single solution is computed, and it avoids becoming trapped in a local optimum, which may happen in other optimization techniques [2].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Adaptive Multi-Population Parallel Genetic Algorithm for Spam Filtering

et al. 2011

JCIT

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Nowadays, e-mail is one of the most inexpensive and expeditious means of communication. However, a principal problem of any internet user is the increasing number of spam, and therefore an efficient spam filtering method is imperative. Feature selection is one of the most important factors, which can influence the classification accuracy rate. To improve the performance of spam prediction, this paper proposes a new fuzzy adaptive multi-population parallel genetic algorithm (FAMGA) for feature selection. To maintain the diversity of population, a few studies of multi-swarm strategy are reported, whereas the dynamic parameter setting has not been considered further. The proposed method is based on multiple subpopulations and each subpopulation runs in independent memory space. For the purpose of controlling the subpopulations adaptively, we put forward two regulation strategies, namely population adjustment and subpopulation adjustment. In subpopulation adjustment, a controller is designed to adjust the crossover rate for each subpopulation, and in population adjustment, a controller is designed to adjust the size of each subpopulation. Three publicly available benchmark corpora for spam filtering, the PU1, Ling-Spam and SpamAssassin, are used in our experiments. The results of experiments show that the proposed method improves the performance of spam filtering, and is significantly better than other feature selection methods. Thus, it is proved that the multi-population regulation strategy can find the optimal feature subset, and prevent premature convergence of the population.

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Sequential multi-criteria feature selection algorithm based on agent genetic algorithm

Cited by 18 publications

References 32 publications

A novel feature selection method based on normalized mutual information

A novel feature selection method based on normalized mutual information

Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves

A Fuzzy Adaptive Multi-Population Parallel Genetic Algorithm for Spam Filtering

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