Second, the K genes are initialized by two ways according to random selection feature and different proportions of selection feature. Finally, the IGWO algorithm selects the optimal classification accuracy and the optimal combination of gene by adjusting the parameters of fitness function. The ...
Exploration of a hybrid feature selection algorithm.In the Knowledge Discovery Process, classification algorithms are often used to help create models with training data that can be used to predict the classes of untested data instances. While there are several factors involved with classification ...
The famous decision tree algorithm was proposed by Quinlan [31]. A decision tree is a series of Boolean tests for the input pattern, and then decided the categories of the pattern. For each test, one best feature will be selected based on information gain or Gini index or others to divide...
The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random for
In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to...
A novel approach for feature selection is introduced in this paper using CHABCF, (Chaotic Artificial Bee Colony based on Fuzzy), algorithm which is a combination of three paradigms: (1) Chaos theory (2) Artificial Bee Colony optimization and (3) Fuzzy logic. The fuzzy logic is used for ...
Feature selection aims to reduce the dimensionality of patterns for clas-sificatory analysis by selecting the most informative rather than irrelevant and/or redundant features. In this study, a hybrid genetic algorithm for feature selection is presented
the correlation-based feature selection technique and the k-means clustering algorithm were used to obtain an optimum feature subset. The probabilistic Naive Bayes (NB) classification method and the decision tree were employed for the classification. Its disadvantage is that, due to its high false-...
Mutual Information is taken as the basic criterion to find the feature relevanc... M Mandal,A Mukhopadhyay - 《Procedia Technology》 被引量: 13发表: 2013年 Feature selection for facial expression recognition based on optimization algorithm This paper presents a wrapper approach to feature selection...
Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization. 展开 关键词: Random forests Imbalance data Intelligence algorithm Feature selection Parameter optimization DOI: 10.1186/s12859-017-1578-z 被引量: 9 ...