Stephen G. Matthews

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Highlights • Recent developments in automatically detecting compromised pig health and welfare.• Five categories of behaviour are reviewed.• Behaviours mapped to sensors that are feasible for automated detection.• Progress towards levels of automation through detection, monitoring and fully automatic detection of behavioural change.• Challenges for automated detection of behavioural changes are multifaceted and require trade-offs to develop such systems.

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules.

Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm

We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multi-objective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.

Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets.

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

Evolving Temporal Association Rules with Genetic Algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty.

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Since animals express their internal state through behaviour, changes in said behaviour may be used to detect early signs of problems, such as in animal health. Continuous observation of livestock by farm staff is impractical in a commercial setting to the degree required to detect behavioural changes relevant for early intervention. An automated monitoring system is developed; it automatically tracks pig movement with depth video cameras, and automatically measures standing, feeding, drinking, and locomotor activities from 3D trajectories. Predictions of standing, feeding, and drinking were validated, but not locomotor activities. An artificial, disruptive challenge; i.e., introduction of a novel object, is used to cause reproducible behavioural changes to enable development of a system to detect the changes automatically. Validation of the automated monitoring system with the controlled challenge study provides a reproducible framework for further development of robust early warning systems for pigs. The automated system is practical in commercial settings because it provides continuous monitoring of multiple behaviours, with metrics of behaviours that may be considered more intuitive and have diagnostic validity. The method has the potential to transform how livestock are monitored, directly impact their health and welfare, and address issues in livestock farming, such as antimicrobial use.

Possibilistic projected categorical clustering via cluster cores

Projected clustering discovers clusters in subsets of locally relevant attributes. There is uncertainty and imprecision about how groups of categorical values are learnt from data for projected clustering and also the data itself. A method is presented for learning discrete possibility distributions of categorical values from data for projected clustering in order to model uncertainty and imprecision. Empirical results show that fewer, more accurate, more compact, and new clusters can be discovered by using possibility distributions of categorical values when compared to an existing method based on Boolean memberships. This potentially allows for new relationships to be identified from data.

Tuning larger membership grades for fuzzy association rules

Sigma count measures scalar cardinality of fuzzy sets. A problem with sigma count is that values of scalar cardinality are calculated entirely from many small membership grades or entirely from few large membership grades. Two novel scalar cardinality measures are proposed for the fitness of a genetic algorithm for tuning membership functions prior to fuzzy association rule mining so that individual membership grades are larger. Preliminary results show a decrease in small membership grades and an increase in large membership grades for fuzzy association rules tested on real-world benchmark datasets.

Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules

Modern organisations now collect very large volumes of data about customers, suppliers and other factors which may impact upon their business. There is a clear need to be able to mine this data and present it to decision makers in a clear and coherent manner. Fuzzy association rules are a popular method to identifying important and meaningful relationships within large data sets. Recently a fuzzy association rule has been proposed that uses the 2-tuple linguistic representation. This paper presents a methodology which makes use of non-stationary fuzzy sets to post process 2-tuple fuzzy association rules reducing the size of the mined rule set by around 20% whilst retaining the semantic meaning of the rule set.