Wesley Romão

Discovering interesting knowledge from a science and technology database with a genetic algorithm

Data mining consists of extracting interesting knowledge from data. This paper addresses the discovery of knowledge in the form of prediction IF-THEN rules, which are a popular form of knowledge representation in data mining. In this context, we propose a genetic algorithm (GA) designed specifically to discover interesting fuzzy prediction rules. The GA searches for prediction rules that are interesting in the sense of being new and surprising for the user. This is done adapting a technique little exploited in the literature, which is based on user-defined general impressions (subjective knowledge). More precisely, a prediction rule is considered interesting (or surprising) to the extent that it represents knowledge that not only was previously unknown by the user but also contradicts his original believes. In addition, the use of fuzzy logic helps to improve the comprehensibility of the rules discovered by the GA. This is due to the use of linguistic terms that are natural for the user. A prototype was implemented and applied to a real-world science & technology database, containing data about the scientific production of researchers. The GA implemented in this prototype was evaluated by comparing it with the J4.8 algorithm, a variant of the well-known C4.5 algorithm. Experiments were carried out to evaluate both the predictive accuracy and the degree of interestingness (or surprisingness) of the rules discovered by both algorithms. The predictive accuracy obtained by the proposed GA was similar to the one obtained by J4.8, but the former, in general, discovered rules with fewer conditions. In addition it works with natural linguistic terms, which leads to the discovery of more comprehensible knowledge. The rules discovered by the proposed GA and the best rules discovered by J4.8 were shown to a user (a University Director) in an interview who evaluated the degree of interestingness (surprisingness) of the rules to him. In general the user considered the rules discovered by the GA much more interesting than the rules discovered by J4.8.

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses’ preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust.

A Variable Neighbourhood Search for Nurse Scheduling with Balanced Preference Satisfaction

The nurse scheduling problem (NSP) is a combinatorial optimisation problem widely tackled in the literature. Recently, a new variant of this problem was proposed, called nurse scheduling problem with balanced preference satisfaction (NSPBPS). This paper further investigates this variant of the NSP as we propose a new algorithm to solve the problem and obtain a better balance of overall preference satisfaction. Initiall, the algorithm converts the problem to a bottleneck assignment problem and solves it to generate an initial feasible solution for the NSPBPS. Posteriorly, the algorithm applies the Variable Neighbourhood Search (VNS) metaheuristic using two sets of search neighbourhoods in order to improve the initial solution. We empirically assess the performance of the algorithm using the NSPLib benchmark instances and we compare our results to other results found in the literature. The proposed VNS algorithm exhibits good performance by achieving solutions that are fairer (in terms of preference satisfaction) for the majority of the scenarios.

Extensions of ant-miner algorithm to deal with class imbalance problem

A database has class imbalance when there are more cases of one class then the others. Classification algorithms are sensitive of this imbalance and tend to valorize the majority classes and ignore the minority classes, which is a problem when the minority classes are the classes of interest. In this paper we propose two extensions of the Ant-Miner algorithm to find better rules to the minority classes. These extensions modify, mainly, how rules are constructed and evaluated. The results show that the proposed algorithms found better rules to the minority classes, considering predictive accuracy and simplicity of the discovered rule list.