Search

A non-preference based pruning algorithm is proposed to rank the Pareto-optimal solutions according to the cost and reliability trade-off for solving multi-objective redundancy allocation problem. The proposed method demonstrates on multi-objective redundancy allocation problem with mixing of non-identical component types in each subsystem. The objectives of system design are to maximize system reliability and minimize system cost simultaneously while satisfying system requirement constraints. Non-dominated sorting genetic algorithm-II (NSGA-II) finds an approximation of Paretooptimal solutions. After obtaining the approximation of Pareto-optimal solutions by NSGA-II, K-means clustering is used to cluster the approximation of Pareto-optimal solutions into some trade-off regions. Thereafter, the Paretooptimal solutions are ranked based on the cost and reliability trade-off compare to the centroid solution of each cluster. The results show that the proposed method is able to identify the most-compromised solution.

Fuzzified game tree search algorithm is based on the idea that the exact game tree evaluation is not required to find the best move. Therefore, pruning techniques may be applied earlier resulting in faster search and greater performance. Applied to an abstract domain, it outperforms the existing ones such as Alpha-Beta, PVS, Negascout, NegaC*, SSS*/ Dual* and MTD(f). In this paper we present experimental results in real domain games, where the proposed algorithm demonstrated 10 percent performance increase over the existing algorithms.

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-12127-2_11, Proceedings of 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010., This article introduces a double pruning algorithm that can be used to reduce the storage requirements, speed-up the classification process and improve the performance of parallel ensembles. A key element in the design of the algorithm is the estimation of the class label that the ensemble assigns to a given test instance by polling only a fraction of its classifiers. Instead of applying this form of dynamical (instance-based) pruning to the original ensemble, we propose to apply it to a subset of classifiers selected using standard ensemble pruning techniques. The pruned subensemble is built by first modifying the order in which classifiers are aggregated in the ensemble and then selecting the first classifiers in the ordered sequence. Experiments in benchmark problems illustrate the improvements that can be obtained with this technique. Specifically, using a bagging ensemble of 101 CART trees as a starting point, only the 21 trees of the pruned ordered ensemble need to be stored in memory. Depending on the classification task, on average, only 5 to 12 of these 21 classifiers are queried to compute the predictions. The generalization performance achieved by this double pruning algorithm is similar to pruned ordered bagging and significantly better than standard bagging.

Ensemble pruning deals with the reduction of base classifiers prior to combination in order to improve generalization and prediction efficiency. Existing ensemble pruning algorithms require much pruning time. This paper presents a fast pruning approach: PMEP (Pattern Mining based Ensemble Pruning). In this algorithm, the prediction results of all base classifiers are organized as a transaction database, and FP-Tree structure is used to compact the prediction results. Then a greedy pattern mining method is explored to find the ensemble of size k . After obtaining the ensembles of all possible sizes, the one with the best accuracy is outputted. Compared with Bagging, GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms.

Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms. All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules.

This work proposes a new rule pruning procedure for Ant-Miner, an Ant Colony algorithm that discovers classification rules in the context of data mining. The performance of Ant-Miner with the new pruning procedure is evaluated and compared with the performance of the original Ant-Miner across several datasets. The results show that the new pruning procedure has a mixed effect on the performance of Ant-Miner. On one hand, overall it tends to decrease the classification accuracy more often than it improves it. On the other hand, the new pruning procedure in general leads to the discovery of classification rules that are considerably shorter, and so simpler (more easily interpretable by the users) than the rules discovered by the original Ant-Miner.

A lot of works address the mining of patterns under constraints. The search space is reduced by taking advantage of pruning conditions on patterns, typically by using anti-monotone and monotone properties. In this paper, we introduce two virtual patterns in order to automatically deduce pruning conditions from any constraint coming from the primitive-based framework which gathers a large set of varied constraints. These virtual patterns enable us to provide negative and positive pruning conditions according to the generalization and the specialization of patterns. We show that these pruning conditions are monotone or anti-monotone and can be pushed into usual constraint mining algorithms. Experiments carried on several contexts show that our proposals improve the mining.

In this paper we investigate the relationship between closed itemset mining, the complete pruning technique and item ordering in the Apriori algorithm. We claim, that when proper item order is used, complete pruning does not necessarily speed up Apriori, and in databases with certain characteristics, pruning increases run time significantly. We also show that if complete pruning is applied, then an intersection-based technique not only results in a faster algorithm, but we get free closed-itemset selection concerning both memory consumption and run-time.

The min-max modular neural network with Gaussian zero-crossing function (M3-GZC) has locally tuned response characteristic and emergent incremental learning ability, but it suffers from quadratic complexity in storage space and response time. Redundant Sample pruning and redundant structure pruning can be considered to overcome these weaknesses. This paper aims at the latter; it analyzes the properties of receptive field in M3-GZC network, and then proposes a strategy for pruning redundant modules. Experiments on both structure pruning and integrated with sample pruning are performed. The results show that our algorithm reduces both the size of the network and the response time notably while not changing the decision boundaries.

Finding all interesting patterns in a database is a data mining task that typically requires a complete search through the hypothesis space. Several ILP systems address this task, e.g., [Deh98, Wro97, FL01]. Safe pruning techniques that reduce the size of the hypothesis space without the risk of missing interesting patterns are very important for this task. This paper is concerned with the effectiveness of pruning techniques in this setting. The addressed pruning techniques are (1) optimum estimates, (2) a pruning technique based on subset tests that is derived from the Apriori search algorithm, (3) pruning based on taxonomies, and (4) to consider only most general patterns as interesting. Methods (1) to (3) are safe pruning techniques that find all interesting patterns; method (4) reduces the number of accepted patterns. The effect of these pruning methods is investigated by experiments within a range of different specific task settings and two databases. Experimental results indicate that optimum estimates and Apriori-style pruning are effective and reliable pruning techniques that produce little additional cost. The effect of taxonomies for pruning is smaller, and it varies over different task settings. In the experiments, the restriction to most general patterns considerably reduces the search costs as well as the set of accepted patterns.

Many researchers have shown that ensemble methods such as Boosting and Bagging improve the accuracy of classification. Boosting and Bagging perform well with unstable learning algorithms such as neural networks or decision trees. Pruning decision tree classifiers is intended to make trees simpler and more comprehensible and avoid over-fitting. However it is known that pruning individual classifiers of an ensemble does not necessarily lead to improved generalisation. Examples of individual tree pruning methods are Minimum Error Pruning (MEP), Error-based Pruning (EBP), Reduced-Error Pruning(REP), Critical Value Pruning (CVP) and Cost-Complexity Pruning (CCP). In this paper, we report the results of applying Boosting and Bagging with these five pruning methods to eleven datasets.

We present a new algorithm for selective search by iterative expansion of leaf nodes. The algorithm reasons with leaf evaluations in a way that leads to high confidence in the choice of move at the root. Performance of the algorithm is measured under a variety of conditions, as compared to minimax with α/β pruning, and to best-first minimax.

Since learning with Inductive Logic Programming (ILP) can be regarded as the search problem through the hypotheses space, it is essential to reduce the search space in order to improve the efficiency. In the propositional learning framework, an efficient admissible search algorithm called OPUS (Optimized Pruning for Unordered Search) has been developed. OPUS employed the effective pruning techniques for unordered search and succeeded in improving the efficiency. In this paper, we propose an application of OPUS to an ILP system Progol. However, because of the difference of representation language, it is not applicable to ILP directly. We make the conditions clear under which the pruning techniques in OPUS can be applied in the framework of Progol. In addition, we propose a new pruning criterion, which can be regarded as inclusive pruning. Experiments are conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms reduce the number of candidate hypotheses to be evaluated as well as the computational time for a certain class of problems.

The efficiency of the αβ-algorithm as a minimax search procedure can be attributed to its effective pruning at so called cut-nodes; ideally only one move is examined there to establish the minimax value. This paper explores the benefits of investing additional search effort at cut-nodes by expanding other move alternatives as well. Our results show a strong correlation between the number of promising move alternatives at cut-nodes and a new principal variation emerging. Furthermore, a new forward pruning method is introduced that uses this additional information to ignore potentially futile subtrees. We also provide experimental results with the new pruning method in the domain of chess.

Post pruning of decision trees has been a successful approach in many real-world experiments, but over all possible concepts it does not bring any inherent improvement to an algorithm's performance. This work explores how a PAC-proven decision tree learning algorithm fares in comparison with two variants of the normal top-down induction of decision trees. The algorithm does not prune its hypothesis per se, but it can be understood to do pre-pruning of the evolving tree. We study a backtracking search algorithm, called Rank, for learning rank-minimal decision trees. Our experiments follow closely those performed by Schaffer [20]. They confirm the main findings of Schaffer: in learning concepts with simple description pruning works, for concepts with a complex description and when all concepts are equally likely pruning is injurious, rather than beneficial, to the average performance of the greedy top-down induction of decision trees. Pre-pruning, as a gentler technique, settles in the average performance in the middle ground between not pruning at all and post pruning.

An efficient parallel algorithm FPM(Fast Parallel Mining) for mining association rules on a shared-nothing parallel system has been proposed. It adopts the count distribution approach and has incorporated two powerful candidate pruning techniques, i.e., distributed pruning and global pruning. It has a simple communication scheme which performs only one round of message exchange in each iteration. We found that the two pruning techniques are very sensitive to data skewness, which describes the degree of non-uniformity of the itemset distribution among the database partitions. Distributed pruning is very effective when data skewness is high. Global pruning is more effective than distributed pruning even for the mild data skewness case. We have implemented the algorithm on an IBM SP2 parallel machine. The performance studies confirm our observation on the relationship between the effectiveness of the two pruning techniques and data skewness. It has also shown that FPM outperforms CD (Count Distribution) consistently, which is a parallel version of the popular Apriori algorithm [2, 3]. Furthermore, FPM has nice parallelism of speedup, scaleup and sizeup.

decision tree classifiers in two learning situations: minimizing loss and probability estimation. In addition to the two most common methods for error minimization, CART'S cost-complexity pruning and C4.5'~ errorbased pruning, we study the extension of cost-complexity pruning to loss and two pruning variants based on Laplace corrections. We perform an empirical comparison of these methods and evaluate them with respect to the following three criteria: loss, mean-squared-error (MSE), and log-loss. We provide a bias-variance decomposition of the MSE to show how pruning affects the bias and variance. We found that applying the Laplace correction to estimate the probability distributions at the leaves was beneficial to all pruning methods, both for loss minimization and for estimating probabilities. Unlike in error minimizat,ion, and somewhat surprisingly, performing no pruning led to results that were on par with other methods in ternis of the evaluation criteria. The main advantage of pruning was in the reduction of the decision tree size, sometimes by a factor of 10. While no method dominated others on all datasets, even for the same domain different pruning mechanisms are better for different loss matrices. We show this last result using Receiver Operating Characteristics (ROC) curves.

An on-line learning algorithm for pruning state space search is described in this paper. The algorithm is based on a finite state machine which is both created and used in the search. The pruning technique is necessary when memory resources in searching huge problem spaces are restricted. A duplicate sequence is a generating path in the search tree that has a counterpart with smaller weight. The automaton provides the dictionary operations Insert and Delete for the duplicate sequences found in the search and Search for pruning the search tree.

The pruning of decision trees often relies on the classification accuracy of the decision tree. In this paper, we show how the misclassification costs, a related criterion applied if errors vary in their costs, can be integrated in several well-known pruning techniques.

This paper presents a study of one particular problem of decision tree induction, namely (post-)pruning, with the aim of finding a common framework for the plethora of pruning methods appeared in literature. Given a tree Tmax to prune, a state space is defined as the set of all subtrees of Tmax to which only one operator, called any-depth branch pruning operator, can be applied in several ways in order to move from one state to another. By introducing an evaluation function f defined on the set of subtrees, the problem of tree pruning can be cast as an optimization problem, and it is also possible to classify each post-pruning method according to both its search strategy and the kind of information exploited by f. Indeed, while some methods use only the training set in order to evaluate the accuracy of a decision tree, other methods exploit an additional pruning set that allows them to get less biased estimates of the predictive accuracy of apruned tree. The introduction of the state space shows that very simple search strategies are used by the postpruning methods considered. Finally, some empirical results allow theoretical observations on strengths and weaknesses of pruning methods to be better understood.

In this paper we introduce a new method for decision tree pruning, based on the minimisation of the expected classification error method by Niblett and Bratko. The original Niblett-Bratko pruning algorithm uses Laplace probability estimates. Here we introduce a new, more general Bayesian approach to estimating probabilities which we call m-probability-estimation. By varying a parameter m in this method, tree pruning can be adjusted to particular properties of the learning domain, such as level of noise. The resulting pruning method improves on the original Niblett-Bratko pruning in the following respects: apriori probabilities can be incorporated into error estimation, several trees pruned to various degrees can be generated, and the degree of pruning is not affected by the number of classes. These improvements are supported by experimental findings. m-probability-estimation also enables the combination of learning data obtained from various sources.