A survey on heterogeneous transfer learning <s> FuzzyTL <s> A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series. > <s> BIB001 </s> A survey on heterogeneous transfer learning <s> FuzzyTL <s> In this paper, the so-called Wang-Mendel (WM) method for generating fuzzy rules from data is enhanced to make it a comprehensive and flexible fuzzy system approach to data description and prediction. In the description part, the core ideas of the WM method are used to develop three methods to extract fuzzy IF-THEN rules from data. The first method shows how to extract rules for the user-specified cases, the second method generates all the rules that can be generated directly from the data, and the third method extrapolates the rules generated by the second method over the entire domain of interest. In the prediction part, two fuzzy predictive models are constructed based on the fuzzy IF-THEN rules extracted by the methods of the description part. The first model gives a continuous output and is suitable for predicting continuous variables, and the second model gives a piecewise constant output and is suitable for predicting categorical variables. We show that by comparing the prediction accuracy of the fuzzy predictive models with different numbers of fuzzy sets covering the input variables, we can rank the importance of the input variables. We also propose an algorithm to optimize the fuzzy predictive models, and show how to use the models to solve pattern recognition problems. Throughout this paper, we use a set of real data from a steel rolling plant to demonstrate the ideas and test the models. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> FuzzyTL <s> By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. <s> BIB003
Shell and Coupland BIB003 proposed an HTL method called Fuzzy Transfer Learning (FuzzyTL) for learning in intelligent environments (IE). Intelligent environments consist of a wide-range of applications where sensors of diverse kinds are placed to gather information about an environment. IE's are often dynamic and transient in nature due to fluid environments and changing conditions which causes uncertain datasets. Each IE is unique due its specific implementation which includes characteristics such as variations in the environments themselves as well as sensor placement, construction, type, and quantity. Thus, when trying to learn a model for a new IE it is challenging to collect sufficient labeled training data to learn an effective model for such IE. Transfer learning can be used in this case to transfer knowledge from labeled data of an existing source IE to enhance building a model for a target IE. The proposed FuzzyTL method utilizes fuzzy logic  as to transfer knowledge from contextually varying environments to model a target task in an IE using labeled source data. Using fuzzy logic as a base, this framework can absorb the inherent uncertainty and dynamic nature of IE's by incorporating approximation and greater expressiveness of such uncertainty exhibited within the data. This proposed method consists of two main parts: learning and adaptation. During the learning phase, the Fuzzy Interface System (FIS) is constructed using the source labeled data, which is the basis for capturing knowledge from the source, and transferring it to the target model. The structure of the FIS consists of fuzzy sets and fuzzy rules which are formulated using an Ad-Hoc Data Driven Learning (ADDL) process and the algorithm by BIB002 BIB001 . During the Adaptation phase, the generated source FIS and the target unlabeled data are used to adapt the fuzzy rule set and the fuzzy rule base from the FIS as to capture variations in the data and assist in bridging the contextual gap between the source and target. Figure 5 , adapted from BIB003 , provides an illustration of the FuzzyTL process to transfer knowledge from the source to the target and predict a value in the target. Experiments were performed using data from a real IE sensor network as for comparing FuzzyTL to ADDL. The results showed that the proposed method outperformed this baseline as it was able to adapt to changes in temporal and situational contexts inherent in IE's.