Xiaolin Jia

Detecting concept drift: An information entropy based method using an adaptive sliding window

Concept drift in data stream poses many challenges and difficulties in mining this tradition-distinct database. In this paper, we focus on detecting concept drift in evolving data stream. We propose a novel method to detect concept drift using entropy over an adaptive sliding window. In the method, the sliding window is not fixed but dynamically determined. Another distinct property is that the method integrates an algorithm to find the exact timestamp for retraining the classifier whenever a concept drift is detected. In the experiments, we evaluate our method on publicly available data streams containing various types of concept drifts, and then compare it with four well known concept drift detection methods. The experimental results show that compared with four benchmarks, the proposed method is better than or comparable with other methods for most cases.

A dissimilarity-based imbalance data classification algorithm

Class imbalances have been reported to compromise the performance of most standard classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this problem, various solutions have been explored mainly via balancing the skewed class distribution or improving the existing classification algorithms. However, these methods pay more attention on the imbalance distribution, ignoring the discriminative ability of features in the context of class imbalance data. In this perspective, a dissimilarity-based method is proposed to deal with the classification of imbalanced data. Our proposed method first removes the useless and redundant features by feature selection from the given data set; and then, extracts representative instances from the reduced data as prototypes; finally, projects the reduced data into a dissimilarity space by constructing new features, and builds the classification model with data in the dissimilarity space. Extensive experiments over 24 benchmark class imbalance data sets show that, compared with seven other imbalance data tackling solutions, our proposed method greatly improves the performance of imbalance learning, and outperforms the other solutions with all given classification algorithms.

LinkLPA: A Link‐based Label Propagation Algorithm for Overlapping Community Detection in Networks

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection, in this article, we propose an algorithm, called link‐based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties. Experimental results on a large number of real‐world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks.

On Participation Constrained Team Formation

The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as ClusterHire. We improve the definition of the ClusterHire problem, and propose an efficient and effective algorithm, entitled Influence. In addition, we place a participation constraint on ClusterHire. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained ClusterHire problem, we devise two algorithms, named ProjectFirst and Era. The former generates a participationconstrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1) Influence performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2) ProjectFirst performs better than Era in terms of time efficiency, yet Era performs better than ProjectFirst in terms of effectiveness.

On Nonredundant Cost-Constrained Team Formation

Due to thewideapplicationof the taskassignmenton the internet, teamformationproblemhas becomeanimportantresearchissue.ArecentlyproposedproblemClusterHireaimstofindateam ofexperts toaccomplishmultipleprojectswhichcanharvest amaximumprofitundera limited budget.However,thereexistredundanciesintheteamyieldedbyexistingalgorithms.Thispaper firststudiesthepropertiesoftheproblem,andgivetwopruningstrategiesbasedonthem.Secondly, aredundancy-eliminatingstrategyandateam-augmentingstrategyareproposed.Inaddition,anew algorithmforgeneratingaprofit-maximizingteamisalsoproposed.Itisbasedontheredundancyeliminatingandteam-augmentingstrategies.Theexperimentalevaluationsshowthatourproposed strategiesandalgorithmsareeffective. KeyWORDS Cluster Hire, Expert-Skill-Project Tripartite Graph, Set Cover, Task Assignment, Team Formation

Grouped Team Formation in Social Networks

Given an expert collaboration social network and a task, the team formation problem in social networks aims at forming a team which satisfies the skill requirements of the task with efficient collaboration. Different communication cost functions have been proposed in the existing work, but the grouped organization structure inside the team is not considered. With a novel communication cost function as objective function, we define the Grouped Team Formation problem. We propose an exact algorithm for solving the problem, and evaluate it by experiments.

Forming Grouped Teams with Efficient Collaboration in Social Networks

Not only the expertise of people but also the collaboration among people are of great importance for a team. Given a set of experts with different skills, a social network that reflects the collaboration among people and a task, the team formation problem in social networks aims at forming a team to complete the task. The team is required to satisfy the skill requirements of the task and collaborates efficiently. Different communication cost functions have been proposed to have a good measure on the collaboration strength of a team in the existing work. However, the grouped organization structure inside team is never considered, which is very common in real life scenarios. In a grouped team, we are more concerned with the collaboration among people in same group and among leaders. In this paper, a novel communication cost function for a grouped team is proposed, and we define the Grouped Team Formation problem. To solve the problem, an exact algorithm is proposed. We further modify the exact algorithm to propose two heuristic algorithms with higher efficiency. Extensive experiments evaluate the effectiveness and efficiency of the proposed methods, and validate the reasonability of our problem definition in practical settings.

Detecting semantic-based communities in node-attributed graphs: Detecting semantic-based communities in node-attributed graphs

In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each nodes perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods.

Cost-minimizing team hires with participation constraint

Team formation, which aims to form a team to complete a given task by covering its required skills, furnishes a natural way to help organizers complete projects effectively. In this work, we propose a new team hiring problem. Given a set of projects P with required skills, and a pool of experts X, each of which has his own skillset, compensation demand and participation constraint (i.e., the maximum number of projects the expert can participate in simultaneously), we seek to hire a team of participation-constrained experts T⊆X to complete all the projects so that the overall compensation is minimized. We refer to this as the participation constrained team hire problem. To the best of our knowledge, this is the first work to investigate the problem. We also study a special case of the problem, where the number of projects is within the participation constraint of each expert and design an exact algorithm for it. Since participation constrained team hire problem is proven to be NP-hard, we design three novel efficient approximate algorithms as its solution, each of which focuses on a particular perspective of the problem. We perform extensive experimental studies, on both synthetic and real datasets, to evaluate the performance of our algorithms. Experimental results show that our exact algorithm far surpasses the brute-force solutions and works well in practice. Besides, the three algorithms behave differently when distinct facets of the problem are involved.

Profit Maximizing Route Recommendation for Vehicle Sharing Requests

Vehicle sharing is a popular and important research in the knowledge discovery community and data mining. In this paper, we proposed a problem that recommends a group of requests to the driver to acquire the maximum profit. Simultaneously, these requests must satisfy some constraints, e.g. the request compatibility and the vehicle capacity. The request compatibility means all the requested routes can be merged into one common route without interruption. The solution to this problem which has three phases including Combination and Pruning, Compatibility Pruning and Recommendation can lead to the optimal result. Extensive experimental results show the effectiveness of problem and the value to the environment protection and economic profits.