ISRN Applied Mathematics
Volume 2014 (2014), Article ID 382738, 11 pages
A Hybrid Feature Selection Method Based on Rough Conditional Mutual Information and Naive Bayesian ClassifierZilin Zeng,1,2 Hongjun Zhang,1 Rui Zhang,1 and Youliang Zhang1
1PLA University of Science & Technology, Nanjing 210007, China
2Nanchang Military Academy, Nanchang 330103, China
Open Access Article
A Rough Hypercuboid Approach for Feature Selection in Approximation Spaces
Issue No.01 - Jan. (2014 vol.26)
Pradipta Maji , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.242
The selection of relevant and significant features is an important problem particularly for data sets with large number of features. In this regard, a new feature selection algorithm is presented based on a rough hypercuboid approach. It selects a set of features from a data set by maximizing the relevance, dependency, and significance of the selected features. By introducing the concept of the hypercuboid equivalence partition matrix, a novel representation of degree of dependency of sample categories on features is proposed to measure the relevance, dependency, and significance of features in approximation spaces. The equivalence partition matrix also offers an efficient way to calculate many more quantitative measures to describe the inexactness of approximate classification. Several quantitative indices are introduced based on the rough hypercuboid approach for evaluating the performance of the proposed method. The superiority of the proposed method over other feature selection methods, in terms of computational complexity and classification accuracy, is established extensively on various real-life data sets of different sizes and dimensions.
Approximation methods, Rough sets, Data analysis, Uncertainty, Data mining, Redundancy,rough hypercuboid approach, Pattern recognition, data mining, feature selection, rough sets
Pradipta Maji, "A Rough Hypercuboid Approach for Feature Selection in Approximation Spaces", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 1, pp. 16-29, Jan. 2014, doi:10.1109/TKDE.2012.242
Economic Modeling Using Artificial Intelligence Methods
Springer Science & Business Media, Apr 2, 2013 - 261 pages
Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena.
The artificial intelligence techniques used to model economic data include:
multi-layer perceptron neural networks
radial basis functions
support vector machines
particle swarm optimization
Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation.
Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.
Advanced Artificial Intelligence
World Scientific, 2011 - 613 pages
Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.
Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
Rough Sets: Current and Future Developments