Text feature selection using ant colony optimization
By Mehdi Hosseinzadeh Aghdam, Nasser Ghasem-Aghaee, Mohammad Ehsan Basiri. Expert systems with applications, 2009
412 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/3e8b83cc4ad65013ca5de3761f8966eac284a320
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bing Xue
- Mengjie Zhang
- Will N. Browne
- Yaochu Jin
- Yingchun Xia
- Xin Yao
- Baharum bin Baharudin
- Xianzhang Shi
In Computer Vision and Pattern Recognition
- Bing Xue
- Mengjie Zhang
- Xin Yao
- Baharum bin Baharudin
- Lam Hong Lee
- Sina Tabakhi
- Lichuan Gu
- Xiaohui Yuan
Citations in the corpus
- Feature selection based on artificial bee colony and gradient boosting decision tree (2019, methodology): “Exemplar methods of wrapper based methods include ant colony optimization (ACO) [19], genetic algorithm (GA) [20], random mutation hill-climbing [21], simulated annealing (SA) [22] and ABC [23].”
- A Survey on Evolutionary Computation Approaches to Feature Selection (2016, background): “algorithms [16], [187], features/nodes are fully connected to each other in the graph, but in [188], each feature was connected only to two features.”
- Feature Selection Using Principal Component Analysis (2010, background): “In the past years many concepts and approaches such as the mutual information [1], feature similarity [2], loss-margin of nearest neighbor classification [3], ant colony optimization [4] and genetic algorithms [5] have been proposed for feature selection.”
- An unsupervised feature selection algorithm based on ant colony optimization (2014, methodology): “, 2004; Skalak, 1994), simulated annealing (Meiri and Zahavi, 2006), genetic algorithm (Sikora and Piramuthu, 2007; Yang and Honavar, 1998), and ant colony optimization (Aghdam et al., 2009). The wrapper approach often provides better performance for a specific classifier in terms of the classificat”
- A Review of Machine Learning Algorithms for Text-Documents Classification (2010, background): “This step is the process of conflating tokens to their root form, e.g. connection to connect, computing to compute etc.”