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This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-series datasets. The analyses demonstrate that the choice of algorithm is task and data specific but that we can derive heuristics for choosing. Gradient Boosting Machines are generally best for classification but there is no single winner for outlier detection though Gradient Boosting Machines (again) and Random Forest are better. Hence, we recommend running evaluations of a number of algorithms using our heuristics.

In this paper, we introduce a neural network-based decision table algorithm. We focus on the implementation details of the decision table algorithm when it is constructed using the neural network. Decision tables are simple supervised classifiers which, Kohavi demonstrated, can outperform state-of-the-art classifiers such as C4.5. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. Initially, we demonstrate how the binary associative-memory neural network can form the decision table index to map between attribute values and data records and subsequently we show how two attribute selection algorithms can be used to pre-select attributes for this decision table. The attribute selection algorithms are easily implemented within the same binary associative-memory framework producing a tightly coupled, two-tier system allowing attribute selection and decision table indexing. The first attribute selector uses mutual information between attributes and classes to select the attributes that classify best. The second attribute selector uses a probabilistic approach to evaluate randomly selected attribute subsets.

Urban traffic control systems such as the widely deployed SCOOT system, incrementally respond to changing traffic conditions. Such systems are often complemented by traffic 2 control centres where road network managers intervene manually to mitigate rapidly developing congestion events. An Intelligent Decision Support (IDS) system developed by the authors within the UK FREEFLOW (FF) project to aid the network managers is presented in this paper.. The primary objective of the FF IDS system is to identify traffic congestion in near-real-time and to recommend appropriate traffic control intervention measures. The FF-IDS consists of multiple internal components. A state estimation component monitors live traffic sensor data and determines if there is a congestion problem on the road network. If a problem is identified, a binary neural pattern-matching component is used to identify past time periods with similar congestion events. This is able to rapidly search large historic traffic datasets finding sets of traffic control interventions carried out during similar historical time periods. The effectiveness of each intervention is evaluated using a Performance Index (PI) and the intervention that resulted in the highest improvement in PI is recommended to network managers. The FF-IDS system can also present traffic incidents and equipment faults that occurred during these historical time periods to the network manager as potential causes of the problem. This paper describes the FF-IDS system in detail. The system is currently under development. An early version of the FF-IDS system was trialled using off-line data from London, yielding encouraging preliminary results.

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus journey times from traffic sensor data and show how attribute selection can both speed our k-NN and increase the prediction accuracy by removing noise and redundant attributes from the data.

In this paper we focus on identifying image structures at different levels in figurative (trademark) images to allow higher level similarity between images to be inferred. To identify image structures at different levels, it is desirable to be able to achieve multiple views of an image at different scales and then extract perceptually-relevant shapes from the different views. The three aims of this work are: to generate multiple views of each image in a principled manner, to identify structures and shapes at different levels within images and to emulate the Gestalt principles to guide shape finding. The proposed integrated approach is able to meet all three aims.