Evolutionary multi-objective optimization (EMO) is often used to deal with practical problems in engineering design to identify products that exhibit the best possible compromise between multiple conflicting performance criteria. Much of the literature in EMO considers algorithmic developments and benchmarking problems involving a single concept only. However, in practice, there could be many applications where the solution of a given problem may be represented using multiple concepts, and optimizing each one of them individually to obtain the overall Pareto front may be inefficient. To address this gap, in this study, we firstly develop computer-aided models of multiple concepts for a simulation-based problem which can serve as a good application of multi-concept optimization. The problem involves the design of lattice structures with different types of unit cells (each constituting a concept), which can be customized to suit a range of real-world applications such as design of structures with minimal thermal expansion, low weight and high rigidity etc. Furthermore, we develop baseline evolutionary strategies to search across multiple concepts simultaneously. Empirical experiments show that the presented strategies are able to outperform conventional single concept-based approach. Moreover, some challenges are also uncovered which prompts the need for further developments.

As the proliferation of constant data feeds increases from social media, embedded sensors, and other sources, the capability to provide predictive concept labels to these data streams will become ever more important and lucrative. However, the dynamic, non-stationary nature, and effectively infinite length of data streams pose additional challenges for stream data mining algorithms. The sparse quantity of training data also limits the use of algorithms that are heavily dependent on supervised training. To address all these issues, we propose an incremental semi-supervised method that provides general concept class label predictions, but it also tracks concept clusters within the feature space using an innovative new online clustering algorithm. Each concept cluster contains an embedded stream classifier, creating a diverse ensemble for data instance classification within the generative model used for detecting emerging concepts in the stream. Unlike other recent novel class detection methods, our method goes beyond detecting, and continues to differentiate and track the emerging concepts. We show the effectiveness of our method on several synthetic and real world data sets, and we compare the results against other leading baseline methods.

Big Data Stream mining has some inherent challenges which are not present in traditional data mining. Not only Big Data Stream receives large volume of data continuously, but also it may have different types of features. Moreover, the concepts and features tend to evolve throughout the stream. Traditional data mining techniques are not sufficient to address these challenges. In our current work, we have designed a multi-tiered ensemble based method HSMiner to address aforementioned challenges to label instances in an evolving Big Data Stream. However, this method requires building large number of AdaBoost ensembles for each of the numeric features after receiving each new data chunk which is very costly. Thus, HSMiner may face scalability issue in case of classifying Big Data Stream. To address this problem, we propose three approaches to build these large number of AdaBoost ensembles using MapReduce based parallelism. We compare each of these approaches from different aspects of design. We also empirically show that, these approaches are very useful for our base method to achieve significant scalability and speedup.

In our current work, we have proposed a multi-tiered ensemble based robust method to address all of the challenges of labeling instances in evolving data stream. Bottleneck of our current work is, it needs to build ADABOOST ensembles for each of the numeric features. This can face scalability issue as number of features can be very large at times in data stream. In this paper, we propose an intelligent approach to build these large number of ADABOOST ensembles with MapReduce based parallelism. We show that, this approach can help our base method to achieve significant scalability without compromising classification accuracy. We analyze different aspects of our design to depict advantages and disadvantages of the approach. We also compare and analyze performance of the proposed approach in terms of execution time, speedup and scale up.

Static data mining assumptions with regard to features and labels often fail the streaming context. Features evolve, concepts drift, and novel classes are introduced. Therefore, any classification algorithm that intends to operate on streaming data must have mechanisms to mitigate the obsolescence of classifiers trained early in the stream. This is typically accomplished by either continually updating a monolithic model, or incrementally updating an ensemble. Traditional static data mining algorithms futile in a streaming context (and often in a distributed sensor network) due to their need to iterate over the entire data set locally. Our approach -- named HSMiner (Hierarchical Stream Miner) -- takes a hierarchical decomposition approach to the ensemble classifier concept. By breaking the classification problem into tiers, we can better prune the irrelevant features and counter individual classification error through weighted voting and boosting. In addition, the atomic decomposition of feature inputs enables straightforward mapping to distributing the ensemble among resources in the network. The implementation proves to be fast and very memory conservative, and we emulate a distributed environment via signal-linked threads. We examine the theoretical and empirical analysis of our approach, specifically examining trade-offs of three different novel class detection variations, and compare these results to a similar method using benchmark data sets.