Alternative splicing plays an important role in regulating gene expression. Currently, most efficient methods use expressed sequence tags or microarray analysis for large-scale detection of alternative splicing. However, it is difficult to detect all alternative splice events with them because of their inherent limitations. Previous computational methods for alternative splicing prediction could only predict particular kinds of alternative splice events. Thus, it would be highly desirable to predict alternative 5'/3' splice sites with various splicing levels using genomic sequences alone. Here, we introduce the competition mechanism of splice sites selection into alternative splice site prediction. This approach allows us to predict not only rarely used but also frequently used alternative splice sites. On a dataset extracted from the AltSplice database, our method correctly classified approximately 70% of the splice sites into alternative and constitutive, as well as approximately 80% of the locations of real competitors for alternative splice sites. It outperforms a method which only considers features extracted from the splice sites themselves. Furthermore, this approach can also predict the changes in activation level arising from mutations in flanking cryptic splice sites of a given splice site. Our approach might be useful for studying alternative splicing in both computational and molecular biology.