k-Optimal: a novel approximate inference algorithm for ProbLog

ProbLog is a probabilistic extension of Prolog. Given the complexity of exact inference under ProbLog??s semantics, in many applications in machine learning approximate inference is necessary. Current approximate inference algorithms for ProbLog however require either dealing with large numbers of proofs or do not guarantee a low approximation error. In this paper we introduce a new approximate inference algorithm which addresses these shortcomings. Given a user-specified parameter k, this algorithm approximates the success probability of a query based on at most k proofs and ensures that the calculated probability p is (1?1/e)p ^???p??p ^?, where p ^? is the highest probability that can be calculated based on any set of k proofs. Furthermore a useful feature of the set of calculated proofs is that it is diverse. Our experiments show the utility of the proposed algorithm.     

Segmentation modeling algorithm: a novel algorithm in data mining

Many enterprises have accumulated a large amount of data over time. To achieve competitive advantages, enterprises need to find effective ways to analyze and understand the vast amounts of raw data they have. Different methods and techniques have been used to reduce the data volume to a manageable level and to help enterprises identify the business value from the data sets. In particular, segmentation methods have been widely used in the area of data mining. In this paper, we present a new algorithm for data segmentation which can be used to build time-dependent customer behavior models. The proposed model has the potential to solve the optimization problem in data segmentation.     

The antigenic index: a novel algorithm for predicting antigenic determinants

In this paper, we introduce a computer algorithm which can be used to predict the topological features of a protein directly from its primary amino acid sequence. The computer program generates values for surface accessibility parameters and combines these values with those obtained for regional backbone flexibility and predicted secondary structure. The output of this algorithm, the antigenic index, is used to create a linear surface contour profile of the protein. Because most, if not all, antigenic sites are located within surface exposed regions of a protein, the program offers a reliable means of predicting potential antigenic determinants. We have tested the ability of this program to generate accurate surface contour profiles and predict antigenic sites from the linear amino acid sequences of well-characterized proteins and found a strong correlation between the predictions of the antigenic index and known structural and biological data.     

MMD-encouraging convolutional autoencoder: a novel classification algorithm for imbalanced data

Applied Intelligence - Imbalanced data classification problem is widely existed in commercial activities and social production. It refers to the scenarios with considerable gap of sample amount...     

VAGA: a novel viscosity-based accelerated gradient algorithm

Proximal Algorithms are known to be very popular in the area of signal processing, image reconstruction, variational inequality and convex optimization due to their small iteration costs and applicability to the non-smooth optimization problems. Various real-world machine learning problems have been solved utilizing the non-smooth convex loss minimization framework, and a recent trend is to design new accelerated algorithms to solve such frameworks efficiently. In this paper, we propose a novel viscosity-based accelerated gradient algorithm (VAGA), that utilizes the concept of viscosity approximation method of fixed point theory for solving the learning problems. We discuss the boundedness of the sequence generated by this iterative algorithm and prove the strong convergence of the algorithm under the few specific conditions. To test the practical performance of the algorithm on real-world problems, we applied it to solve the regularized multitask regression problem with sparsity-inducing regularizers. We present the detailed comparative analysis of our algorithm with few traditional proximal algorithms on three real benchmark multitask regression datasets. We also apply the proposed algorithm to the task of joint splice-site recognition problem of bio-informatics. The improved results demonstrate the efficacy of our algorithm over state-of-the-art proximal gradient descent algorithms. To the best of our knowledge, it is the first time that a viscosity-based iterative algorithm is applied to solve the real world problem of regression and recognition.     

A novel algorithm for discrete-event simulation: asynchronousdistributed discrete-event simulation algorithm for cyclic circuitsusing a dataflow network

Distributed simulation of circuits in which the process interactions form a cyclic graph is addressed. The method described uses a dataflow network synthesized on the basis of the connectivity of the circuit components. The algorithm, called Yaddes (which stands for yet another asynchronous distributed discrete-event simulation algorithm), computes for each component a quantity time of next event, which permits the corresponding model to execute asynchronously as far ahead in simulation time as possible. The network ensures that a simulation process executing in a distributed processing environment will not deadlock. The algorithm, which also offers acceptable performance and provable correctness, is compared with the two other principal algorithms proposed to avoid deadlocks: the deadlock recovery algorithm and the exception-mode algorithm. Performance results for Yaddes are presented     

MultiK-MHKS: a novel multiple kernel learning algorithm

In this paper, we develop a new effective multiple kernel learning algorithm. First, map the input data into m different feature spaces by m empirical kernels, where each generatedfeature space is takenas one viewof the input space. Then through the borrowing the motivating argument from Canonical Correlation Analysis (CCA)that can maximally correlate the m views in the transformed coordinates, we introduce a special term called Inter-Function Similarity Loss R IFSL into the existing regularization framework so as to guarantee the agreement of multi-view outputs. In implementation, we select the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the incorporated paradigm, and the experimental results on benchmark data sets demonstrate the feasibility and effectiveness of the proposed algorithm named MultiK-MHKS.     

FastGen:一种新颖的混合的结构产生算法

结构产生算法是计算机辅助结构解析专家系统的心脏。本文首先分析了结构产生过程的复杂性,然后介绍了一个新颖的,混合使用结构组装与结构缩简策略的结构产生算法。这个名为FastGen的算法的一个最大特点是能够直接地、前瞻性地使用必需的和禁止的两种子结构,而且允许子结构之间有任何程度的相互重迭。     

WSM: a novel algorithm for subgraph matching in large weighted graphs

Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph, the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum. Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions. Nonetheless, complex biological problems such as disease gene prioritization and conserved phylogenetic tree construction largely depend on the similarity calculation among the networks. Although several existing methods provide efficient methods for graph and subgraph similarity measurement, they produce nonintuitive results due to the underlying unweighted graph model assumption. Moreover, very few algorithms exist for weighted graph matching that are applicable with the restriction that the data and pattern graph sizes are equal. In this paper, we introduce a novel algorithm for weighted subgraph matching which can effectively be applied to directed/undirected weighted subgraph matching. Experimental results demonstrate the superiority and relative scalability of the algorithm over available state of the art methods.     

Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum (minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization (SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.     

Efficient algorithm for a novel pattern of time series

In this paper, a novel kind of threshold similarity query is introduced. It reports a threshold if exceeding which the queried time series has the most similar time intervals compared to that of the given query time series above its query threshold, and the extent of the similarity between the two time interval sequences should be within a user-specified range. We present an efficient method composed by two steps to solve the query. The first step is to dramatically narrow the search space into a quite small subspace without false dismissals, and the second to search iteratively in the subspace. In more detail, a lower bounding distance function is described, which guarantees no false dismissals during the first step. Furthermore, we use binary search to quickly locate the solution within the subspace based on the continuity and monotone of the length function of time intervals, which are proved in this paper. We implemented our method on traffic data and discovered some useful knowledge. We also carried out experiments on diverse time series data to compare our method with brute force method. The results were excellent: our method accelerated the search time from 10 times up to 150 times.     

On a novel unsupervised competitive learning algorithm for scalarquantization

This letter presents a novel unsupervised competitive learning rule called the boundary adaptation rule (BAR), for scalar quantization. It is shown both mathematically and by simulations that BAR converges to equiprobable quantizations of univariate probability density functions and that, in this way, it outperforms other unsupervised competitive learning rules.     

Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo.

     

TimeFly algorithm: a novel behavior-inspired movie recommendation paradigm

Pattern Analysis and Applications - This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, which works on the idea of altering behavior of the user with...     

Sensor fusion by a novel algorithm for time delay estimation

Time delay estimation (TDE) is a growing area of mathematical research, finding applications in a wide range of fields including medical imaging and sensor fusion. Numerous TDE algorithms have been constructed, often in response to particular real-world problems. A sensor fusion problem for localising a mobile robot has previously arisen for which Zaman created an appropriate TDE algorithm and made conjectures from the data the algorithm produced. The algorithm is novel in that it can synchronise data streams to guaranteed bounds from discrete sensor readings alone. A new algorithm was needed for the mobile robot problem because the sensors were commercial off-the-shelf (COTS) products manufactured to different specifications. They took readings at different frequencies and their clocks were independent. The increasing dissemination of COTS products is likely to lead to further applications for Zaman?s algorithm. In this paper we have given the algorithm a rigorous grounding, proving that it converges to estimates of sub-sample accuracy. We have also numerically investigated convergence rates and shown how results from a real-world robot experiment resemble corresponding simulations.     

A parallel sorting algorithm for a novel model of computation

The computational complexity of a parallel algorithm depends critically on the model of computation. We describe a simple and elegant rule-based model of computation in which processors apply rules asynchronously to pairs of objects from a global object space. Application of a rule to a pair of objects results in the creation of a new object if the objects satisfy the guard of the rule. The model can be efficiently implemented as a novel MIMD array processor architecture, the Intersecting Broadcast Machine. For this model of computation, we describe an efficient parallel sorting algorithm based on mergesort. The computational complexity of the sorting algorithm isO(nlog² n), comparable to that for specialized sorting networks and an improvement on theO(n ^1.5) complexity of conventional mesh-connected array processors.     

Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.     

A novel hybrid algorithm for feature selection

Feature selection is an important filtering method for data analysis, pattern classification, data mining, and so on. Feature selection reduces the number of features by removing irrelevant and redundant data. In this paper, we propose a hybrid filter–wrapper feature subset selection algorithm called the maximum Spearman minimum covariance cuckoo search (MSMCCS). First, based on Spearman and covariance, a filter algorithm is proposed called maximum Spearman minimum covariance (MSMC). Second, three parameters are proposed in MSMC to adjust the weights of the correlation and redundancy, improve the relevance of feature subsets, and reduce the redundancy. Third, in the improved cuckoo search algorithm, a weighted combination strategy is used to select candidate feature subsets, a crossover mutation concept is used to adjust the candidate feature subsets, and finally, the filtered features are selected into optimal feature subsets. Therefore, the MSMCCS combines the efficiency of filters with the greater accuracy of wrappers. Experimental results on eight common data sets from the University of California at Irvine Machine Learning Repository showed that the MSMCCS algorithm had better classification accuracy than the seven wrapper methods, the one filter method, and the two hybrid methods. Furthermore, the proposed algorithm achieved preferable performance on the Wilcoxon signed-rank test and the sensitivity–specificity test.     

GAKREM: A novel hybrid clustering algorithm

We introduce a novel clustering algorithm named GAKREM (Genetic Algorithm K-means Logarithmic Regression Expectation Maximization) that combines the best characteristics of the K-means and EM algorithms but avoids their weaknesses such as the need to specify a priori the number of clusters, termination in local optima, and lengthy computations. To achieve these goals, genetic algorithms for estimating parameters and initializing starting points for the EM are used first. Second, the log-likelihood of each configuration of parameters and the number of clusters resulting from the EM is used as the fitness value for each chromosome in the population. The novelty of GAKREM is that in each evolving generation it efficiently approximates the log-likelihood for each chromosome using logarithmic regression instead of running the conventional EM algorithm until its convergence. Another novelty is the use of K-means to initially assign data points to clusters. The algorithm is evaluated by comparing its performance with the conventional EM algorithm, the K-means algorithm, and the likelihood cross-validation technique on several datasets.