The Management of Context-Sensitive Features: A Review of Strategies <s> Implicit Context <s> Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to (1) be defined with respect to the same set of relevant attributes, (2) be disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We describe a supervised, incremental, instance-based learning algorithm (Bloom) that removes these assumptions. Bloom learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept descriptions. <s> BIB001 </s> The Management of Context-Sensitive Features: A Review of Strategies <s> Implicit Context <s> Abstract In this paper a new concept of Floating Approximation is introduced. It is based on Pawlaks theory of Rough Sets and the existence of ‘hidden attributes’ in knowledge representation systems. After explaining the motivation, a simplified algorithm developed and implemented by the author is described. First experience is reported and some ideas for further research are suggested. <s> BIB002 </s> The Management of Context-Sensitive Features: A Review of Strategies <s> Implicit Context <s> A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method. > <s> BIB003
So far, we have been concerned with data in which contextual features are explicitly represented. Unfortunately, contextual information is often omitted from a dataset. Because we tend to take context for granted, we neglect to record the context of an observation. Fortunately, it is sometimes possible to recover contextual information. In this section, we consider two methods for recovering missing (hidden, implicit) contextual features. First, unsupervised clustering algorithms may be able to recover lost context BIB001   . Second, the temporal sequence of the instances may imply contextual information BIB002  BIB003 . We believe that clusters that are generated by unsupervised clustering algorithms typically capture shared context. That is, if two cases are assigned to the same cluster, then they likely share similar contexts. Therefore, if we cluster cases by their primary features, then members of the same cluster will tend to belong to the same class and the same context. More precisely, the likelihood that they belong to the same class and context is greater than the likelihood for the samples from the general population. If we are given a dataset where there are only primary features, because the importance of contextual features was overlooked when the data were collected, we can use a clustering algorithm to recreate the missing contextual features. For example, we can label each case according to the cluster in which it belongs, and then we can introduce a new contextual feature of the form Cluster = Label. An alternative approach would be to integrate a form of clus- tering with a concept learning algorithm, instead of separating the clustering process from the classification process. This approach has been used by several researchers, with some success BIB001   . A feature of the form Cluster = Label might not be purely contextual, since clusters may be predictive of the class. Some of the success of approaches that combine clustering and classification may be due to this. Further research is required to determine whether clusters tend to be contextual or primary. Another way to recover lost contextual information is to use temporal information, if it is available. We believe that events that occur close together in time tend to share context. If the records in a database contain a field for the date, this information might be used to expose hidden contextual information. We could introduce a new feature of the form Time = Date. Depending on what strategy we use for handling context, it may be useful to convert the time into a discrete feature. In incremental learning, the order in which examples are encountered by the learner may correspond to the timing of the examples. In batch learning, the order of the examples in the file may correspond to the timing. We can introduce a new feature of the form Order = Number. Again, it may be useful to discretize this feature. We believe that the FLORA algorithm (an incremental algorithm) is implicitly using the order of the examples to recover lost contextual information BIB002  BIB003 . The FLORA algorithm is essentially an instance of the contextual classifier selection strategy (Strategy 3 in Section 2). The context is used to select the appropriate classifier from a set of possible classifiers. The interesting innovation is that the context is implied in the order of presentation of the examples.