Heuristic target class selection for advancing performance of coverage-based rule learning

Rule learning is a popular branch of machine learning, which can provide accurate and interpretable classification results. In general, two main strategies of rule learning are referred to as 'divide and conquer' and 'separate and con-quer'. Decision tree generation that follows the former strategy has a serious drawback, which is known as the replicated sub-tree problem, resulting from the constraint that all branches of a decision tree must have one or more common attributes. The above problem is likely to result in high computational complexity and the risk of overfitting, which leads to the necessity to develop rule learning algorithms (e.g., Prism) that follow the separate and conquer strategy. The replicated sub-tree problem can be effectively solved using the Prism algorithm , but the trained models are still complex due to the need of training an independent rule set for each selected target class. In order to reduce the risk of overfitting and the model complexity, we propose in this paper a variant of the Prism algorithm referred to as PrismCTC. The experimental results show that the PrismCTC algorithm leads to advances in classification performance and reduction of model complexity, in comparison with the C4.5 and Prism algorithms

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

TOWARDS BUILDING AN INTELLIGENT INTEGRATED MULTI-MODE TIME DIARY SURVEY FRAMEWORK

Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems can be leveraged for use in TDS. The motivation for this work is from realizing the opportunity that an enhanced web based instrument can offer the survey domain to unite the various facets of web based surveys to create an intelligent integrated multi-mode TDS framework. We envision the framework to provide all the advantages of web based surveys and interviewer assisted surveys. The two primary challenges are in determining what data is to be used by the system and how to interact with the user – specifically integrating the (1) Interviewer-assisted mode, and (2) Self-administered mode. Our proposed solution – the intelligent integrated multi-mode framework – is essentially the solution to a set of modeling problems and we propose two sets of overreaching mechanisms: (1) Knowledge Engineering Mechanisms (KEM), and (2) Interaction Mechanisms (IxM), where KEM serves the purpose of understanding what data can be created, used and stored while IxM deals with interacting with the user. We build and study a prototype instrument in the interviewer-assisted mode based on the framework. We are able to determine that the instrument improves the interview process as intended and increases the data quality of the response data and is able to assist the interviewer. We also observe that the framework’s mechanisms contribute towards reducing interviewers’ cognitive load, data entry times and interview time by predicting the next activity. Advisor: Leenkiat So

Learning fuzzy decision trees

We present a recurrent neural network which learns to suggest the next move during the descent along the branches of a decision tree. More precisely, given a decision instance represented by a node in the decision tree, the network provides the degree of membership of each possible move to the fuzzy set >. These fuzzy values constitute the core of the probability of selecting the move out of the set of the children of the current node. This results in a natural way for driving the sharp discrete- state process running along the decision tree by means of incremental methods on the continuous-valued parameters of the neural network. The bulk of the learning problem consists in stating useful links between the local decisions about the next move and the global decisions about the suitability of the final solution. The peculiarity of the learning task is that the network has to deal explicitly with the twofold charge of lighting up the best solution and generating the move sequence that leads to that solution. We tested various options for the learning procedure on the problem of disambiguating natural language sentences

Learning Fuzzy Decision Trees

We present a recurrent neural network which learns to suggest the next move during the descent along the branches of a decision tree. More precisely, given a decision instance represented by a node in the decision tree, the network provides the degree of membership of each possible move to the fuzzy set good move». These fuzzy values constitute the core of the probability of selecting the move out of the set of the children of the current node. This results in a natural way for driving the sharp discrete-state process running along the decision tree by means of incremental methods on the continuous-valued parameters of the neural network. The bulk of the learning problem consists in stating useful links between the local decisions about the next move and the global decisions about the suitability of the final solution. The peculiarity of the learning task is that the network has to deal explicitly with the twofold charge of lighting up the best solution and generating the move sequence..

Learning fuzzy decision trees using integer programming

A popular method in machine learning for supervised classification is a decision tree. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver. We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data