Machine Learning with Automatic Feature Selection for Multi-class Protein Fold Classification
黃淳德(Chuen-Der Huang)；梁勝富(Sheng-Fu Liang)；林進燈(Chin-Teng Lin)；吳瑞成(Ruei-Cheng Wu)
machine learning ； hierarchical architecture ； feature selection ； gate ； neural network ； protein fold ； bioinformatics
Journal of Information Science and Engineering
|Volume or Term/Year and Month of Publication||
21卷4期（2005 / 07 / 01）
711 - 720
In machine learning, both the properly used networks and the selected features are important factors which should be considered carefully. These two factors will influence the result, whether for better or worse. In bioinformatics, the amount of features may be very large to make machine learning possible. In this study we introduce the idea of feature selection in the problem of bioinformatics. We use neural networks to complete our task where each input node is associated with a gate. At the beginning of the training, all gates are almost closed, and, at this time, no features are allowed to enter the network. During the training phase, gates are either opened or closed, depending on the requirements. After the selection training phase has completed, gates corresponding to the helpful features are completely opened while gates corresponding to the useless features are closed more tightly. Some gates may be partially open, depending on the importance of the corresponding features. So, the network can not only select features in an online manner during learning, but it also does some feature extraction. We combine feature selection with our novel hierarchical machine learning architecture and apply it to multi-class protein fold classification. At the first level the network classifies the data into four major folds: all alpha, all beta, alpha+beta and alpha beta. In the next level, we have another set of networks which further classifies the data into twenty-seven folds. This approach helps achieve the following. The gating network is found to reduce the number of features drastically. It is interesting to observe that, for the first level using just 50 features selected by the gating network, we can get a test accuracy comparable to that using 125 features in neural classifiers. The process also helps us get a better insight into the folding process. For example, tracking the evolution of different gates, we can find which characteristics (features) of the data are more important for the folding process. Eventually, it reduces the computation time. The use of the hierarchical architecture helps us get a better performance also.