Description:
Low-resolution protein models are often used within a hierarchical framework for structure prediction. However, even with these simplified but realistic protein models, the search for the optimal solution remains NP complete. The complexity is further compounded by the multimodal nature of the search space. In this paper, we propose a systematic design of an evolutionary search technique, namely the memetic algorithm (MA), to effectively search the vast search space by exploiting the domain-specific knowledge and taking cognizance of the multimodal nature of the search space. The proposed MA achieves this by incorporating various novel features: 1) a modified fitness function includes two additional terms to account for the hydrophobic and polar nature of the residues; 2) a systematic (rather than random) generation of population automatically prevents an occurrence of invalid conformations; 3) a generalized nonisomorphic encoding scheme implicitly eliminates generation of twins (similar conformations) in the population; 4) the identification of a meme (protein substructures) during optimization from different basins of attraction - a process that is equivalent to implicit applications of threading principles; 5) a clustering of the population corresponds to basins of attraction that allows evolution to overcome the complexity of multimodal search space, thereby avoiding search getting trapped in a local optimum; and 6) a 2-stage framework gathers domain knowledge (i.e., substructures or memes) from different basins of attraction for a combined execution in the second stage. Experiments conducted with different lattice models using known benchmark protein sequences and comparisons carried out with recently reported approaches in this journal show that the proposed algorithm has robustness, speed, accuracy, and superior performance. The approach is generic and can easily be extended for applications to other classes of problems.
Description:
In Bayesian classifier learning, estimating the joint probability distribution (,) or the likelihood (|) directly from training data is considered to be difficult, especially in large multidimensional data sets. To circumvent this difficulty, existing Bayesian classifiers such as Naive Bayes, BayesNet, and ADE have focused on estimating simplified surrogates of (,) from different forms of one‐dimensional likelihoods. Contrary to the perceived difficulty in multidimensional likelihood estimation, we present a simple generic ensemble approach to estimate multidimensional likelihood directly from data. The idea is to aggregate (|) estimated from a random subsample of data . This article presents two ways to estimate multidimensional likelihoods using the proposed generic approach and introduces two new Bayesian classifiers called and that estimate (|) using a nearest‐neighbor density estimation and a probability estimation through feature space partitioning, respectively. Unlike the existing Bayesian classifiers, ENNBayes and MassBayes have constant training time and space complexities and they scale better than existing Bayesian classifiers in very large data sets. Our empirical evaluation shows that ENNBayes and MassBayes yield better predictive accuracy than the existing Bayesian classifiers in benchmark data sets.
Description:
The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets.
Description:
Despite their wide spread use, nearest neighbour density estimators have two fundamental limitations: O(n2) time complexity and O(n) space complexity. Both limitations constrain nearest neighbour density estimators to small data sets only. Recent progress using indexing schemes has improved to near linear time complexity only.We propose a new approach, called LiNearN for Linear time Nearest Neighbour algorithm, that yields the first nearest neighbour density estimator having O(n) time complexity and constant space complexity, as far as we know. This is achieved without using any indexing scheme because LiNearN uses a subsampling approach for which the subsample values are significantly less than the data size. Like existing density estimators, our asymptotic analysis reveals that the new density estimator has a parameter to trade off between bias and variance. We show that algorithms based on the new nearest neighbour density estimator can easily scale up to data sets with millions of instances in anomaly detection and clustering tasks. Highlights•Reject the premise that a NN algorithm must find the NN for every instance.•The first NN density estimator that has O(n) time complexity and O(1) space complexity.•These complexities are achieved without using any indexing scheme.•Our asymptotic analysis reveals that it trades off between bias and variance.•Easily scales up to large data sets in anomaly detection and clustering tasks.
Description:
Clusters may exist in different subspaces of a multidimensional dataset. Traditional full-space clustering algorithms have difficulty in identifying these clusters. Various subspace clustering algorithms have used different subspace search strategies. They require clustering to assess whether cluster(s) exist in a subspace. In addition, all of them perform clustering by measuring similarity between points in the given feature space. As a result, the subspace selection and clustering processes are tightly coupled. In this paper, we propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces). It enables neighbouring core points to be clustered based on the number of subspaces they share. It explicitly splits candidate subspace selection and clustering into two separate processes, enabling different types of cluster definitions to be employed easily. Through extensive experiments on synthetic and real-world datasets, we demonstrate that CSSub discovers non-redundant subspace clusters with arbitrary shapes in noisy data; and it significantly outperforms existing state-of-the-art subspace clustering algorithms.
Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Description:
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.