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Connect6 is an intuitive and fair game with no first-hand advantage. Because of the large search tree, the search depth is often lower, to sacrifice strength of chess to improve search efficiency. In this case, this paper proposes an optimization algorithm for pruning Connect6 search tree based on the principal variation search and transposition tables algorithms. The optimization algorithm obtained PV node through searching, narrowed the pruning interval, and stored the key value of the situation into the transposition table through searching, and then directly returned the evaluation value when the same chess game was searched again, which greatly improved the search efficiency. Through a large number of repeated experiments, the effectiveness of the algorithm can be proved.

Minimax Algorithm, is a solution to reduce the burden on hardware in chess engine. However, a more in-depth method is needed to further increase the search algorithm. One of those solutions is called Alpha-Beta Pruning algorithm. The idea is to eliminate the unnecessary nodes in the search tree.

Tree-based methods are a non-parametric modelling strategy that can be used in combination with generalized linear models or Cox proportional hazards models, mostly at an exploratory stage. Their popularity is mainly due to the simplicity of the technique along with the ease in which the resulting model can be interpreted. Variable selection bias from variables with many possible splits or missing values has been identified as one of the problems associated with tree-based methods. A number of unbiased recursive partitioning algorithms have been proposed that avoid this bias by using p -values in the splitting procedure of the algorithm. The final tree is obtained using direct stopping rules (pre-pruning strategy) or by growing a large tree first and pruning it afterwards (post-pruning). Some of the drawbacks of pre-pruned trees based on p -values in the presence of interaction effects and a large number of explanatory variables are discussed, and a simple alternative post-pruning solution is presented that allows the identification of such interactions. The proposed method includes a novel pruning algorithm that uses a false discovery rate (FDR) controlling procedure for the determination of splits corresponding to significant tests. The new approach is demonstrated with simulated and real-life examples.

Entropy-based pruning has a limit in selecting a fine distribution of phrase pairs to be pruned in a threshold.Changing the distribution through other divergence metrics improves pruning efficiency in our preliminary empirical analysis.Derived problematic factors are fixed divergence distribution and missing impact of word-coupling strength.We propose a fine pruning method using two parameters to control the factors and analyze their effects to divergence change.It improves pruning efficiency compared with Entropy-based pruning in practical translations of English, Spanish, and French. A widely used automatic translation approach, phrase-based statistical machine translation, learns a probabilistic translation model composed of phrases from a large parallel corpus with a large language model. The translation model is often enormous because of many combinations of source and target phrases, which leads to the restriction of applications to limited computing environments. Entropy-based pruning resolves this issue by reducing the model size while retaining the translation quality. To safely reduce the size, this method detects redundant components by evaluating a relative entropy of models before and after pruning the components. In the literature, this method is effective, but we have observed that it can be improved more by adjusting the divergence distribution determined by the relative entropy. In the results of preliminary experiments, we derive two factors responsible for limiting pruning efficiency of entropy-based pruning. The first factor is proportion of pairs composing translation models with respect to their translation probability and its estimate. The second factor is the exponential increase of the divergence for pairs with low translation probability and estimate. To control the factors, we propose a divergence-based fine pruning using a divergence metric to adapt the curvature change of the boundary conditions for pruning and Laplace smoothing. In practical translation tasks for English-Spanish and English-French language pairs, this method shows statistically significant improvement on the efficiency up to 50% and average 12% more pruning compared to entropy-based pruning to show the same translation quality.

Learning from imbalanced data is attracting an increasing interest by the machine learning community. This is mainly due to the high number of real applications that are affected by this situation. The adaptation of the standard decision trees to deal with imbalanced data represents one of the important number of approaches that have been developed to address this problem. This adaptation has been proposed under three different perspectives: splitting criterion, assignment rule and pruning. In this paper, we focus our attention to the pruning of decision trees. We propose an adaptation of the standard pruning algorithm MCCP to address the skewed-data problem. Our contribution affects two levels: adaption of the metric used in selecting nodes to be firstly pruned and change of the evaluation measure used in selecting the best decision-tree through the pruning set. Our goal is to show that, contrary to the popular belief in the literature enquiring into the uselessness of decision tree pruning, an adaptive pruning technique for imbalanced situations is more efficient and more accurate towards the minority class. A total of twelve binary class data-sets having different imbalance ratio are used to test the performance of the proposed method. Experimental results show that the proposed post-pruning approach can increase the performance of imbalanced decision trees in terms of evaluation measures that are recent and appropriate for the context of imbalanced classification.

Branch-and-bound approaches are promising in pruning fruitless search space during the resource constrained scheduling. However, such approaches only compare the estimated upper and lower bounds of an incomplete schedule to the length of the best feasible schedule at that iteration, which does not fully exploit the potential of the pruning during the search. Aiming to improve the performance of resource constrained scheduling, this paper proposes a parallel structure-aware pruning approach that can traverse the search space significantly faster than state-of-the-art branch-and-bound techniques. This paper makes three major contributions: i) it proposes an efficient pruning technique using the structural scheduling information of the obtained best feasible schedules; ii) it investigates how to perform parallel search to enable efficient multi-directional search and generation of effective fences by tuning the operation enumeration order; and iii) it presents a framework that supports the sharing of minimum upper-bound and fence information among different search tasks to enable efficient parallel structure-aware pruning. The experimental results demonstrate that our parallel pruning approach can drastically reduce the overall resource constrained scheduling time under a wide variety of resource constraints.

The continuous non-revisiting genetic algorithm (cNrGA) uses the entire search history and parameter-less adaptive mutation to significantly enhance search performance. Storing the entire search history is natural and costs little when the number of fitness evaluations is small or moderate. However, if the number of evaluations required is substantial, some memory management is desirable. In this paper, we propose two pruning mechanisms to keep the memory used constant. They are least recently used pruning and random pruning. The basic idea is to prune a unit of memory when the memory threshold is reached and some new search information is required to be stored, thus keeping the overall memory used constant. Meanwhile, both pruning strategies naturally form parameter-less adaptive mutation operators. A study is carried out to evaluate the impact on performance caused by loss of search history information. Experimental results show that (1) both strategies can maintain the performance of cNrGA, up to the empirical limit when 90 % of the search history is not recorded, (2) cNrGA and its variants with constant memory outperform the real-coded genetic algorithm and the standard particle swarm optimization. By pre-extracting all the current prune-able history information and storing them into a list, namely, to-prune-list, the overhead of both pruning strategies becomes small. This suggests that cNrGA can be extended to use in situations when the number of fitness evaluations is much larger than before with no significant effect on statistical performance. This widens the applicability of cNrGA to include more practical problems that require larger number of fitness evaluations before converging to the global optima.

Although ordering-based pruning algorithms possess relatively high efficiency, there remains room for further improvement. To this end, this paper describes the combination of a dynamic programming technique with the ensemble-pruning problem. We incorporate dynamic programming into the classical ordering-based ensemble-pruning algorithm with complementariness measure (ComEP), and, with the help of two auxiliary tables, propose a reasonably efficient dynamic form, which we refer to as ComDPEP. To examine the performance of the proposed algorithm, we conduct a series of simulations on four benchmark classification datasets. The experimental results demonstrate the significantly higher efficiency of ComDPEP over the classic ComEP algorithm. The proposed ComDPEP algorithm also outperforms two other state-of-the-art ordering-based ensemble-pruning algorithms, which use uncertainty weighted accuracy and reduce-error pruning, respectively, as their measures. It is noteworthy that, the effectiveness of ComDPEP is just the same with that of the classical ComEP algorithm.

intention of MapReduce Sets for Pruning Decomposition expressions analysis has to suggest criteria how Pruning Decomposition expressions in Pruning Decomposition data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Pruning Decomposition Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Pruning Decomposition process that are expressiveness of Pruning Decomposition models and reproducibility of their Pruning Decomposition inference. Pruning Decomposition expressions are assumed to be elements of a Pruning Decomposition expression space or Conjecture class and Pruning Decomposition data provide "information" which of these Pruning Decomposition expressions should be used to interpret the Pruning Decomposition data. An inference Pruning Decomposition algorithm constructs the mapping between Pruning Decomposition data and Pruning Decomposition expressions, in particular by a Pruning Decomposition cost minimization process. Fluctuations in the Pruning Decomposition data often limit the Pruning Decomposition precision, which we can achieve to uniquely identify a single Pruning Decomposition expression as interpretation of the Pruning Decomposition data. We advocate an information theoretic perspective on Pruning Decomposition expression analysis to resolve this dilemma where the tradeoff between Pruning Decomposition informativeness of statistical inference Pruning Decomposition and their Pruning Decomposition stability is mirrored in the information-theoretic Pruning Decomposition optimum of high Pruning Decomposition information rate and zero communication expression error. The inference Pruning Decomposition algorithm is considered as an outlier object Pruning Decomposition path, which naturally limits the resolution of the Pruning Decomposition expression space given the uncertainty of the Pruning Decomposition data. KeywordsPruning Decomposition expressions, kernel function.

Data mining is a knowledge discovery process that analyzes data and generate useful pattern from it. Classification is the technique that uses pre-classified examples to classify the required results. Decision tree is used to model classification process. Using feature values of instances, Decision trees classify those instances. Each node in a decision tree represents a feature in an instance to be classified. In this research work ID3, C4.5 and C5.0 Compare with each other. Among all these classifiers C5.0 gives more accurate and efficient result. This research work used C5.0 as the base classifier so proposed system will classify the result set with high accuracy and low memory usage. The classification process generates fewer rules compare to other techniques so the proposed system has low memory usage. Error rate is low so accuracy in result set is high and pruned tree is generated so the system generates fast results as compare with other technique. In this research work proposed system use C5.0 classifier that Performs feature selection and reduced error pruning techniques which are described in this paper. Feature selection technique assumes that the data contains many redundant features. so remove that features which provides no useful information in any context. Select relevant features which are useful in model construction. Crossvalidation method gives more reliable estimate of predictive. Over fitting problem of the decision tree is solved by using reduced error pruning technique. With the proposed system achieve 1 to 3% of accuracy, reduced error rate and decision tree is construed within less time.

In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. The existing algorithms, either maintaining too many candidates, or requiring assistant structures built on the specific attribute subset, or returning results with probabilistic guarantee, cannot process top-k query on massive data efficiently. This paper proposes a sorted-list-based TKAP algorithm, which utilizes some data structures of low space overhead, to efficiently compute top-k results on massive data. In round-robin retrieval on sorted lists, TKAP performs adaptive pruning operation and maintains the required candidates until the stop condition is satisfied. The adaptive pruning operation can be adjusted by the information obtained in round-robin retrieval to achieve a better pruning effect. The adaptive pruning rule is developed in this paper, along with its theoretical analysis. The extensive experimental results, conducted on synthetic and real-life data sets, show the significant advantage of TKAP over the existing algorithms.

We address the problem of index pruning without compromising the quality of ad-hoc similarity search among sets and ranked lists. We discuss three different ways to prune the index structure and, by linking the index structure with the concept of Locality Sensitive Hashing (LSH), we introduce two solutions to query processing over the pruned index. Through a probabilistic analysis we ensure that a user-defined recall goal is still guaranteed. We are able to formulate an optimization problem that can determine the optimal pruning factor for all three pruning methods. The experimental evaluations over real-world data validate that the optimal pruning factor indeed ensures the recall goal without any significant effect on the quality of similarity search on a much smaller index.

Background: We study an important problem of similarity grouping processing on stream data that inherently contain uncertainty. Method: In this paper SBSP - [Stage by Stage Pruning] a novel pruning method is proposed for fast, accurate clustering and classifying the data where the two stages were grouped into a single framework MYFRAME. Findings : The proposed approach group the data-by-data level pruning using Manhattan distance in first stage. In the second stage, the data is grouped by object level pruning in hyperspace. Improvements: Currently, this approach is applied in real time applications such as object detection, video retrieval, people detection and tracking, earth quake monitoring etc.

The continuous Non-revisiting Genetic Algorithm (cNrGA) uses the entire search history and parameter-less adaptive mutation to significantly enhance search performance. Experimental results show that it has better performance than Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state of the art evolutionary algorithm. Storing the search history is natural and costs little when fitness evaluations are expensive. However, if the number of evaluations required is substantial, some memory management is desirable. In this paper, we propose two pruning mechanisms to keep the memory used constant. They are Random pruning and Least Recently Used pruning. The idea is to prune a node when a memory threshold is reached and a new node is required to be inserted, thus keeping the overall memory used constant. Experimental results show that both strategies can maintain the performance of cNrGA, up to the limit when 90% of the nodes are not recorded. This suggests that cNrGA can be extended to use in situations when the number of fitness evaluations are much larger than before with no significant effect on statistical performance, which widens the applicability of cNrGA to include more practical problems that require larger number of fitness evaluations before converging to the global optimum.

Cost-sensitive decision trees have a great success in building models for classification tasks in data mining and machine learning. Decision tree pruning technique is regarded as an important component of the optimization of decision tree. However, many previous cost-sensitive decision trees researches focus on minimizing the misclassification rate and removing nodes when the cost of the pruned tree is less than the original one. In this paper, we propose an effective method for building cost-sensitive decision tree and a probabilistic pruning mechanism for the decision tree. There are two major contributions of this paper. Firstly, the cost-sensitive decision tree is built to make predictions to minimize the total cost of test costs and different costs associated with different types of misclassification. Compared with existing models, leaves are labeled by minimizing the total cost instead of the majority class. Secondly, we design a probabilistic pruning mechanism where the pruning probability is related to the change of costs around pruning. The pruning results yield worse performance on the training set, but better performance on the testing set. Therefore, the probabilistic pruning mechanism improves the performance of the cost-sensitive decision tree. Experimental results show the efficiency of the probabilistic pruning mechanism for cost-sensitive decision tree.

Aiming at how to find the possible existence of the threat paths in Connect6, this paper puts forward to a new searching algorithm — Alpha-Beta-TSS. The algorithm couples Alpha-Beta Pruning Search to TSS (Threat Space Search). It has the strong ability of solving and the solution has a high accuracy.

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.