Evolving text classification rules with genetic programming

We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications

Machine learning methods for detecting structure in metabolic flow networks

Metabolic flow networks are large scale, mechanistic biological models with good predictive power. However, even when they provide good predictions, interpreting the meaning of their structure can be very difficult, especially for large networks which model entire organisms. This is an underaddressed problem in general, and the analytic techniques that exist currently are difficult to combine with experimental data. The central hypothesis of this thesis is that statistical analysis of large datasets of simulated metabolic fluxes is an effective way to gain insight into the structure of metabolic networks. These datasets can be either simulated or experimental, allowing insight on real world data while retaining the large sample sizes only easily possible via simulation. This work demonstrates that this approach can yield results in detecting structure in both a population of solutions and in the network itself. This work begins with a taxonomy of sampling methods over metabolic networks, before introducing three case studies, of different sampling strategies. Two of these case studies represent, to my knowledge, the largest datasets of their kind, at around half a million points each. This required the creation of custom software to achieve this in a reasonable time frame, and is necessary due to the high dimensionality of the sample space. Next, a number of techniques are described which operate on smaller datasets. These techniques, focused on pairwise comparison, show what can be achieved with these smaller datasets, and how in these cases, visualisation techniques are applicable which do not have simple analogues with larger datasets. In the next chapter, Similarity Network Fusion is used for the first time to cluster organisms across several levels of biological organisation, resulting in the detection of discrete, quantised biological states in the underlying datasets. This quantisation effect was maintained across both real biological data and Monte-Carlo simulated data, with related underlying biological correlates, implying that this behaviour stems from the network structure itself, rather than from the genetic or regulatory mechanisms that would normally be assumed. Finally, Hierarchical Block Matrices are used as a model of multi-level network structure, by clustering reactions using a variety of distance metrics: first standard network distance measures, then by Local Network Learning, a novel approach of measuring connection strength via the gain in predictive power of each node on its neighbourhood. The clusters uncovered using this approach are validated against pre-existing subsystem labels and found to outperform alternative techniques. Overall this thesis represents a significant new approach to metabolic network structure detection, as both a theoretical framework and as technological tools, which can readily be expanded to cover other classes of multilayer network, an under explored datatype across a wide variety of contexts. In addition to the new techniques for metabolic network structure detection introduced, this research has proved fruitful both in its use in applied biological research and in terms of the software developed, which is experiencing substantial usage.EPSR

Partitioned Sampling of Public Opinions Based on Their Social Dynamics

Public opinion polling is usually done by random sampling from the entire population, treating individual opinions as independent. In the real world, individuals' opinions are often correlated, e.g., among friends in a social network. In this paper, we explore the idea of partitioned sampling, which partitions individuals with high opinion similarities into groups and then samples every group separately to obtain an accurate estimate of the population opinion. We rigorously formulate the above idea as an optimization problem. We then show that the simple partitions which contain only one sample in each group are always better, and reduce finding the optimal simple partition to a well-studied Min-r-Partition problem. We adapt an approximation algorithm and a heuristic algorithm to solve the optimization problem. Moreover, to obtain opinion similarity efficiently, we adapt a well-known opinion evolution model to characterize social interactions, and provide an exact computation of opinion similarities based on the model. We use both synthetic and real-world datasets to demonstrate that the partitioned sampling method results in significant improvement in sampling quality and it is robust when some opinion similarities are inaccurate or even missing

A new approach for discovering business process models from event logs.

Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learnings since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered ascross-sectional.This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with time-varying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research; Business; Processes; Event; Information; Information systems; Systems; Learning; Data; Behavior; Patterns; IT; Event calculus; Knowledge; Database; Noise;