Dendritic Cell Algorithm (DCA) is a binary classifier in the category of artificial immune systems. During its pre-processing phase, DCA requires features to be mapped into three signal categories including safe signal, pathogenic associated molecular pattern, and danger signal, which is usually referred to as signal categorisation. Conventionally, feature-to-signal mapping is performed either manually or automatically by using dimension reduction or feature selection techniques such as principal component analysis and fuzzy rough set theory. The former has been criticised for its potential over-fitting, whilst the latter may suffer from either the loss of underlying feature meaning or impractical for large and complex datasets. This work therefore investigate the necessity of the signal categorisation process by proposing a DCA without the use of signal categorisation but with generalised context detection functions, where the more complex parameters of these functions are learned using the genetic algorithm. This is followed by a comparative study on twelve well-known datasets; the experimental results show overall better performances in terms of accuracy, sensitivity and specificity compared to the conventional DCAs. This confirms that the signal categorisation phase is not necessary, if the weights of the generalised context detection functions can be optimised.