Naive Bayes is renowned for its simplicity and efficiency, occupying a significant position within the domains of data mining and machine learning. However, its performance optimisation is constrained by the adequacy of instance training and the effectiveness of feature selection. To address these limitations, this paper first proposes a novel algorithm called Misclassified Instances Re-training Naive Bayes (MIReNB). This methodology employs Leave-One-Out Cross Validation (LOOCV) to meticulously discern and iteratively reuse misclassified instances, dynamically adjusting the frequency table of the conditional probability distribution for each attribute-value and class, followed by retraining procedures to augment predictive performance. Building upon MIReNB, we subsequently propose two extensions. The first extension, designated as MIReNB Select, incorporates an additional feature selection stage before identifying misclassified instances to determine the optimal feature subset for retraining, thereby improving prediction accuracy and robustness. The second extension, termed MIReNB Twice, involves a dual iterative process that deeply integrates misclassified instances, achieving adaptive refinement of the conditional probability distribution while enhancing classification accuracy and generalisation ability. Numerous empirical investigations have shown that MIReNB, along with its two extensions, can enhance classification accuracy notably while preserving the inherent simplicity and efficiency of the Naive Bayes classifier.

Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged n-Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts.

Workload characteristics are vital for both data center operation and job scheduling in co-located data centers, where online services and batch jobs are deployed on the same production cluster. In this article, a comprehensive analysis is conducted on Alibaba's cluster-trace-v2018 of a production cluster of 4034 machines. The findings and insights are the following: (1) The workload on the production cluster poses a daily cyclical fluctuation, in terms of CPU and disk I/O utilization, and the memory system has become the performance bottleneck of a co-located cluster. (2) Batch jobs including their tasks and derived instances can be approximated as Zipf distribution. However, for all batch jobs with directed acyclic graph dependency, they suffer from co-location with online services since the online services are highly prioritized. (3) The resource usages of containers have similar cyclical fluctuation consistent with the whole cluster, while their memory usages remain approximately constant. (4) The number of batch jobs co-located with online services is dependent on the mispredictions per kilo instructions of online services. In order to guarantee the QoS of online services, when the MPKI of online services rises, the number of batch jobs to be co-located on the same machine should decrease.

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

More and more applications have come with large data sets in the past decade. However, existing algorithms cannot guarantee to scale well on large data. Averaged n-Dependence Estimators (AnDE) allows for flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for large data learning. In this paper, we propose a sample-based attribute selection technique for AnDE. It needs one more pass through the training data, in which a multitude of approximate AnDE models are built and efficiently assessed by leave-one-out cross validation. The use of a sample reduces the training time. Experiments on 15 large data sets demonstrate that the proposed technique significantly reduces AnDE's error at the cost of a modest increase in training time. This efficient and scalable out-of-core approach delivers superior or comparable performance to typical in-core Bayesian network classifiers.

Ever increasing data quantity makes ever more urgent the need for highly scalable learners that have good classification performance. Therefore, an out-of-core learner with excellent time and space complexity, along with high expressivity (that is, capacity to learn very complex multivariate probability distributions) is extremely desirable. This paper presents such a learner. We propose an extension to the $k$-dependence Bayesian classifier (KDB) that discriminatively selects a sub- model of a full KDB classifier. It requires only one additional pass through the training data, making it a three-pass learner. Our extensive experimental evaluation on $16$ large data sets reveals that this out-of-core algorithm achieves competitive classification performance, and substantially better training and classification time than state-of-the-art in-core learners such as random forest and linear and non-linear logistic regression.

It is necessary to provide collaborative design environment for preliminary design of complex products. For the specific requirements in preliminary design, this paper proposes the collaborative design environment for preliminary design of complex products(CDEPD). Agent-based architecture is adopted in CDEPD and it can support cooperative work for distributed designers from multiple disciplines. Based on GPGP from K. Decker, belief commitment-based coordination mechanism is proposed in this paper and it has been applied in CDEPD.