When labeling objects via Internet-based outsourcing systems, the labelers may have bias, because they lack expertise, dedication and personal preference. These reasons cause Imbalanced Multiple Noisy Labeling. To deal with the imbalance labeling issue, we propose an agnostic algorithm PLAT (Positive LAbel frequency Threshold) which does not need any information about quality of labelers and underlying class distribution. Simulations on eight real-world datasets with different underlying class distributions demonstrate that PLAT not only effectively deals with the imbalanced multiple noisy labeling problem that off-the-shelf agnostic methods cannot cope with, but also performs nearly the same as majority voting under the circumstances that labelers have no bias.

In this paper, we present an adaptive graph-based personalized recommendation method based on co-ranking and query-based collaborative diffusion. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships and analyzing the representation of user’s preference in the graph. The experiments show that this algorithm can outperform the traditional CF methods and achieve competitive performance compared with many model-based and graph-based recommendation methods, and have better scalability and flexibility.

In this paper, we propose an approach to build a multi-way concept hierarchy from a text corpus, which is based on WordNet and multi-way hierarchical clustering. In addition, a new evaluation metric is presented, and our approach is compared with 4 kinds of existing methods on the Amazon Customer Review data set.

Digital media tend to combine text and images to express richer information, especially on image hosting and online shopping websites. This trend presents a challenge in understanding the contents from different forms of information. Features representing visual information are usually sparse in high dimensional space, which makes the learning process intractable. In order to understand text and its related visual information, we present a new graphical model-based approach to discover more meaningful information in rich media. We extend the standard Latent Dirichlet Allocation (LDA) framework to learn in high dimensional feature spaces.

Since their popularity began to rise in the mid-2000s there has been significant growth in the number of multi-core and multi-processor computers available. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs by treating propositional nodes as tasks which can be scheduled to operate upon messages sent between nodes via the arcs that already exist as part of the propositional graph representation. The use of scheduling heuristics within a prioritized message passing architecture allows inference graphs to perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.

Modern science relies on simulation techniques for understanding phenomenon, exploring design options, or evaluating models. Assuring the correctness of simulators is a key problem where a multitude of solutions ranging from manual inspection to formal verification are applicable. Formal verification incorporates the rigor necessary but not all simulators are generated from formal specifications. Manual inspection is readily available but lacks the rigor and is prone to errors. In this paper, we describe an automated verification system (AVS) where the constraints that the system must adhere to are specified by the user in general purpose first-order logic. AVS translates these constraints into a verification program that scans the simulator traceand verifies that no constraints are violated. Computer microarchitecture simulations were successfully used to demonstrate the proposed approach. This paper describes the preliminary results and discusses how artificial intelligence techniques can be used to facilitate effective run-time verification of simulators.

Standard feature selection algorithms deal with given candidate feature sets at the individual feature level. When features exhibit certain group structures, it is beneficial to conduct feature selection in a grouped manner. For high-dimensional features, it could be far more preferable to online generate and process features one at a time rather than wait for generating all features before learning begins. In this paper, we discuss a new and interesting problem of online group feature selection from feature streams at both the group and individual feature levels simultaneously from a feature stream. Extensive experiments on both real-world and synthetic datasets demonstrate the superiority of the proposed algorithm.  

The accuracy of subchloroplast location prediction algorithms often depends on predictive and succinct features derived from proteins. Thus, to improve the prediction accuracy, this paper proposes a novel SubChloroplast location prediction method, called SCHOTS, which integrates the HOmolog knowledge Transfer and feature Selection methods. SCHOTS contains two stages. First, discriminating features are generated by WS-LCHI, a Weighted Gene Ontology (GO) transfer model based on bit-Score of proteins and Logarithmic transformation of CHI-square. Second, the more informative GO terms are selected from the features. Extensive studies conducted on three real datasets demonstrate that SCHOTS outperforms three off-the-shelf subchloroplast prediction methods.

Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effective ensemble learning methods for improving the performance of prediction models; however, there is a major drawback on extremely imbalanced data-sets. It is unclear under which conditions bagging is outperformed by other sampling schemes in terms of imbalanced classification. These issues motivate us to propose a novel approach, unevenly balanced bagging (UBagging) to boost the performance of the prediction model for imbalanced binary classification. Our experimental results demonstrate that UBagging is effective and statistically significantly superior to single learner decision trees J48 (SingleJ48), bagging, and equally balanced bagging (BBagging) on 32 imbalanced data-sets.

Training intelligent systems is a time consuming and costly process that often limits their application to real-world problems. Prior work in crowdsourcing has attempted to compensate for this challenge by generating sets of labeled training data for machine learning algorithms. In this work, we seek to move beyond collecting just statistical data and explore how to gather structured, relational representations of a scenario using the crowd. We focus on activity recognition because of its broad applicability, high level of variation between individual instances, and difficulty of training systems a priori. We present ARchitect, a system that uses the crowd to ascertain pre and post conditions for actions observed in a video and find relations between actions. Our ultimate goal is to identify multiple valid execution paths from a single set of observations, which suggests one-off learning from the crowd is possible.