Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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Feature selection is the prerequisite condition of predicting the activity of drugs accurately and quickly. In this paper, we use a method similar to Hybrid model, which combines Filter and Wrapper model, to select the best feature subset and use the subset to build the model of predicting activity of cyclooxygenase-2. First of all we extract the ten features that might influence the activity of cox-2 inhibitors. We get the general trend of data by Principal Component Analysis and get the contribution value of each feature by Multiple Linear Regression Analysis, and then we can remove those irrelevant or redundant features from the initial feature set and get three possible best feature subsets. In order to decide which subset should be selected, we build two classification models, BP Neural Network and Support Vector Machine, to predict the activity of cox-2 inhibitors, and then we can evaluate the feature subsets and find the best feature subset to predict the activity of cox-2 inhibitors.