1 |
Front Matter |
|
|
Abstract
|
2 |
|
|
|
Abstract
|
3 |
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label |
Arjun Pakrashi,Brian Mac Namee |
|
Abstract
In multi-label classification a datapoint can be labelled with more than one class at the same time. A common but trivial approach to multi-label classification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate hyperparameter tuning, a computationally expensive optimisation problem. Tuning is important to train a good multi-label classifier model. There is a scarcity in the literature of effective multi-label classification approaches that do not require extensive hyperparameter tuning. This paper addresses this scarcity by proposing CascadeML, a multi-label classification approach based on cascade neural network that takes label associations into account and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need
|
4 |
|
|
|
Abstract
|
5 |
Purity Filtering: An Instance Selection Method for Support Vector Machines |
David Morán-Pomés,Lluís A. Belanche-Muñoz |
|
Abstract
Support Vector Machines can achieve levels of accuracy comparable to those achieved by Artificial Neural Networks, but they are also slower to train. In this paper a new algorithm, called Purity Filtering, is presented, designed to filter training data for binary classification SVMs, in order to choose an approximation of the data subset that is more relevant to the training process..The proposed algorithm is parametrized so to allow a regulation of both spatial and temporal complexity, adapting to the needs and possibilities of each execution environment. A user-specified parameter, the purity, is used to indirectly regulate the number of filtered data, even though the algorithm has also been adapted to let the user directly specify the number of filtered data. Using this algorithm with real datasets, reductions up to 75% of training data (using only 25% of the data samples to train) were achieved with no major loss on the quality of classification.
|
6 |
Towards Model-Based Reinforcement Learning for Industry-Near Environments |
Per-Arne Andersen,Morten Goodwin,Ole-Christoffer Granmo |
|
Abstract
Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. Although these algorithms are fundamentally different, both suffer from high variance, low sample efficiency, and hyperparameter sensitivity that, in practice, make these algorithms a no-go for critical operations in the industry..On the other hand, model-based reinforcement learning focuses on learning the transition dynamics between states in an environment. If the environment dynamics are adequately learned, a model-based approach is perhaps the most sample efficient method for learning agents to act in an environment optimally. The traits of model-based reinforcement are ideal for real-world environments where sampling is slow and in mission-critical operations. In the warehouse industry, there is an increasing motivation to minimise time and to maximise production. In many of these environments, the literature suggests that the autonomous
|
7 |
Stepwise Evolutionary Learning Using Deep Learned Guidance Functions |
Colin G. Johnson |
|
Abstract
This paper explores how Learned Guidance Functions (LGFs)—a pre-training method used to smooth search landscapes—can be used as a fitness function for evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscrambling the Rubik’s Cube. Comparisons are made with a previous LGF approach based on random forests, and with a baseline approach based on traditional error-based fitness.
|
8 |
Monotonicity Detection and Enforcement in Longitudinal Classification |
Sergey Ovchinnik,Fernando E. B. Otero,Alex A. Freitas |
|
Abstract
Longitudinal datasets contain repeated measurements of the same variables at different points in time, which can be used by researchers to discover useful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and applying them in longitudinal classification model construction. Two different approaches were used to detect monotonic relations and include them into the classification task. The proposed approaches are evaluated using data from the English Longitudinal Study of Ageing (ELSA) with 10 different age-related diseases used as class variables to be predicted. A gradient boosting algorithm (XGBoost) is used for constructing classification models in two scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a pos
|
9 |
Understanding Structure of Concurrent Actions |
Perusha Moodley,Benjamin Rosman,Xia Hong |
|
Abstract
Whereas most work in reinforcement learning (RL) ignores the structure or relationships between actions, in this paper we show that exploiting structure in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a combinatorial explosion of the action space..This paper proposes two methods: a first approach uses implicit structure to perform high-level action elimination using task-invariant actions; a second approach looks for more explicit structure in the form of action clusters. Both methods are context-free, focusing only on an analysis of the action space and show a significant improvement in policy convergence times.
|
10 |
|
|
|
Abstract
|
11 |
Demonstrating the Distinctions Between Persuasion and Deliberation Dialogues |
Yanko Kirchev,Katie Atkinson,Trevor Bench-Capon |
|
Abstract
A successful dialogue requires that the participants have a shared understanding of what they are trying to achieve, individually and collectively. This coordination can be achieved if both recognise the type of dialogue in which they are engaged. We focus on two particular dialogue types, action persuasion and deliberation dialogues, which are often conflated because they share similar speech acts. Previously, a clear distinction was made between the two in terms of the different pre- and post-conditions used for the speech acts within these dialogues. This prior work gave formal specifications of the dialogue moves within the dialogues but offered no evaluation through implementation. In this paper, we present an implementation to demonstrate that the two dialogue types described in this way can be realised in software to support focussed communication between autonomous agents. We provide the design and implementation details of our new tool along with an evaluation of the software. The tool we have produced captures the distinctive features of each of the two dialogue types, to make plain their differences and to validate the speech acts for use in practical scenarios.
|
12 |
Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities |
Ryan Colin Gibson,Matt-Mouley Bouamrane,Mark D. Dunlop |
|
Abstract
Patients with Learning Disabilities (LD) have substantial and unmet healthcare needs, and previous studies have highlighted that they face both health inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations - typically less than ten minutes. In order to alleviate this issue, we propose a digital communication aid in the form of an ontology-based medical questionnaire that can adapt to a patient’s medical context as well as their accessibility needs (physical and cognitive). The application is intended to be used in advance of a consultation so that a primary care practitioner may have prior access to their LD patients’ self-reported symptoms. This work builds upon and extends previous research carried out in the development of adaptive medical questionnaires to include interactive and interface functionalities designed specifically to cater for patients with potentially complex accessibility needs. A patient’s current health status and accessibility profile (relating to their impairment
|
13 |
Exposing Knowledge: Providing a Real-Time View of the Domain Under Study for Students |
Omar Zammit,Serengul Smith,Clifford De Raffaele,Miltos Petridis |
|
Abstract
With the amount of information that exists online, it is impossible for a student to find relevant information or stay focused on the domain under study. Research showed that search engines have deficiencies that might prevent students from finding relevant information. To this end, this research proposes a technical solution that takes the personal search history of a student into consideration and provides a holistic view of the domain under study. Based on algorithmic approaches to assert semantic similarity, the proposed framework makes use of a user interface to dynamically assist students through aggregated results and wordcloud visualizations. The effectiveness of our approach is finally evaluated through the use of commonly used datasets and compared in line with existing research.
|
14 |
|
|
|
Abstract
|
15 |
A General Approach to Exploit Model Predictive Control for Guiding Automated Planning Search in Hybr |
Faizan Bhatti,Diane Kitchin,Mauro Vallati |
|
Abstract
Automated planning techniques are increasingly exploited in real-world applications, thanks to their flexibility and robustness. Hybrid domains, those that require to reason both with discrete and continuous aspects, are particularly challenging to handle with existing planning approaches due to their complex dynamics. In this paper we present a general approach that allows to combine the strengths of automated planning and control systems to support reasoning in hybrid domains. In particular, we propose an architecture to integrate Model Predictive Control (MPC) techniques from the field of control systems into an automated planner, to guide the effective exploration of the search space.
|
16 |
A Tsetlin Machine with Multigranular Clauses |
Saeed Rahimi Gorji,Ole-Christoffer Granmo,Adrian Phoulady,Morten Goodwin |
|
Abstract
The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the . hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it turns out that there is significantly less hyper-parameter tuning involved in applying the MTM to new problems. Further, we demonstrate empirically that the MTM provides similar performance to what is achieved with a finely specificity-optimized TM, by comparing their performance on both synthetic and real-world datasets.
|
17 |
Building Knowledge Intensive Architectures for Heterogeneous NLP Workflows |
Kareem Amin,Stelios Kapetanakis,Nikolaos Polatidis,Klaus-Dieter Althoff,Andreas Denge,Miltos Petridi |
|
Abstract
Workflows are core part of every modern organization ensuring smooth running operations, task consistency and process automation. Dynamic workflows are being used increasingly due to their flexibility in a working environment where they minimize mundane tasks like long-term maintenance and increase productivity by automatically responding to changes and introducing new processes. Constant changes within unstable environments where information may be sparse, inconsistent and uncertain can create a bottleneck to a workflow in predicting behaviours effectively. Within a business environment, automatic applications like customer support, complex incidents can be regarded as instances of a dynamic process since mitigation policies have to be responsive and adequate to any case no matter its unique nature. Support engineers work with any means at their disposal to solve any emerging case and define a custom prioritization strategy, to achieve the best possible result. This paper describes a novel workflow architecture for heavy knowledge-related application workflows to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how policies can be gen
|
18 |
WVD: A New Synthetic Dataset for Video-Based Violence Detection |
Muhammad Shahroz Nadeem,Virginia N. L. Franqueira,Fatih Kurugollu,Xiaojun Zhai |
|
Abstract
Violence detection is becoming increasingly relevant in many areas such as for automatic content filtering, video surveillance and law enforcement. Existing datasets and methods discriminate between violent and non-violent scenes based on very abstract definitions of violence. Available datasets, such as “Hockey Fight” and “Movies”, only contain fight versus non-fight videos; no weapons are discriminated in them. In this paper, we focus explicitly on weapon-based fighting sequences and propose a new dataset based on the popular action-adventure video game Grand Theft Auto-V (GTA-V). This new dataset is called “Weapon Violence Dataset” (WVD). The choice for a virtual dataset follows a trend which allows creating and labelling as sophisticated and large volume, yet realistic, datasets as possible. Furthermore, WVD also avoids the drawbacks of access to real data and potential implications. To the best of our knowledge no similar dataset, that captures weapon-based violence, exists. The paper evaluates the proposed dataset by utilising local feature descriptors using an SVM classifier. The extracted features are aggregated using the Bag of Visual Word (BoVW) technique to classify weap
|
19 |
|
|
|
Abstract
|
20 |
Evolving Prediction Models with Genetic Algorithm to Forecast Vehicle Volume in a Service Station (B |
Himadri Sikhar Khargharia,Siddhartha Shakya,Russell Ainslie,Gilbert Owusu |
|
Abstract
In the service industry, having an efficient resource plan is of utmost importance for operational efficiency. An accurate forecast of demand is crucial in obtaining a resource plan which is efficient. In this paper, we present a real world application of an AI forecasting model for vehicle volumes forecasting in service stations. We improve on a previously proposed approach by intelligently tuning the hyper parameters of the prediction model, taking into account the variability of the vehicle volume data in a service station. In particular, we build a Genetic algorithm based model to find the topology of the neural network and also to tune additional parameters of the prediction model that is related to data filtration, correction and feature selection. We compare our results with the results from ad hoc parameter settings of the model from previous work and show that the combined genetic algorithm and neural network based approach further improves forecasting accuracy which helps service stations better manage their resource requirements.
|