the proceedings contain 37 papers. the special focus in this conference is on Innovative Techniques and Applications of Artificial Intelligence. the topics include: the dreaming variational autoencoder for reinforceme...
ISBN:
(纸本)9783030041908
the proceedings contain 37 papers. the special focus in this conference is on Innovative Techniques and Applications of Artificial Intelligence. the topics include: the dreaming variational autoencoder for reinforcement learning environments;abnormality detection in the cloud using correlated performance metrics;Directed recursion search: A directed DFS for online pathfinding in random grid-based environments;Modelling trust between users and AI;multi-criteria decision making with existential rules using repair techniques;gramError: A quality metric for machine generated songs;computational complexity analysis of decision tree algorithms;forecasting student’s preference in E-learning systems;human motion recognition using 3D-Skeleton-data and neural networks;implementing rules with artificial neurons;autonomous swarm agents using case-based reasoning;beat the bookmaker – winning football bets withmachinelearning (best application paper);rule-mining and clustering in business process analysis;machinelearning in control systems: An overview of the state of the art;Predicting fluid work demand in service organizations using AI techniques;workforce rostering via metaheuristics;incorporating risk in field services operational planning process;risk information recommendation for engineering workers;generalised decision level ensemble method for classifying multi-media data;spotting earnings manipulation: Using machinelearning for financial fraud detection;informed pair selection for self-paced metric learning in siamese neural networks;context extraction for aspect-based sentiment analytics: Combining syntactic, lexical and sentiment knowledge;confidence in random forest for performance optimization;designing a website using a genetic algorithm;regulated information sharing and patternrecognition for smart cities;a middleware to link lego mindstorms robots with4th generation language software NetLogo.
Ultrasound testing is a popular technique to find some hidden rail damages. In this paper we focus on the modern Russian railway flaw detectors, such as AVICON-14, which produce the results of ultrasound testing in th...
详细信息
Given a beginning and ending document, automated storytelling attempts to fill in intermediary documents to form a coherent story. this is a common problem for analysts;they often have two snippets of information and ...
详细信息
this paper presents an enhancement to a prior work on interactive software customization. By conserving the historical data of belief states collected during a dialogue and analyzing the dataset with wavelet transform...
详细信息
Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in datamining and useful in various field including patternrecognition, machine learnin...
详细信息
ISBN:
(纸本)9781538632574
Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in datamining and useful in various field including patternrecognition, machinelearning, information retrieval and so on. As an extensive studied area, many clustering methods are proposed in literature. Among them, some methods are focused on mining clusters with arbitrary shapes. However, when dealing with large-scale and multi-dimensional data, there is still a need for an efficient and versatile clustering method to identify these arbitrary shapes that may be embedded in these multidimensional space. In this paper, we propose a density-based clustering algorithm that adopts a divide-and-conquer strategy. To handle large-scale and multi-dimensional data, we first divide the data by grid cells. It is very efficient in large-scale cases where other algorithms often fail. Moreover, rather than tuning the grid cell width, we present a way to automatically determine the grid cell width. then, we propose a flood-filling like algorithm to identify the clusters with arbitrary shapes over these grid cells. Finally, extensive experiments are conducted in both synthetic databases and real-world databases, showing that the proposed algorithm efficiently finds accurate clusters in both low-dimensional and multi-dimensional databases.
In this paper, global-level view-invariant descriptors for human action recognition using 3D reconstruction data are proposed. 3D reconstruction techniques are employed for addressing two of the most challenging issue...
详细信息
the majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end...
详细信息
ISBN:
(数字)9783319419206
ISBN:
(纸本)9783319419206;9783319419190
the majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end users cannot act upon them seamlessly. Furthermore, interdisciplinary knowledge can not be obtained from domain-specific silo of data. the emergence of Linked data (LD) as a new model for knowledge representation, which intertwines data with its semantics, has introduced new opportunities for data miners. Accordingly, this paper proposes an ontology-based Semantic-Aware Bayesian network (BN) model. In contraxt to the exisiting mining algorithms, the proposed model does nto transorm the original format of the LD set. therefore, it not only accomodates the sematnic aspects in LD, but also caters to the need of connectign different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising perfomance.
this paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to ...
详细信息
ISBN:
(纸本)9783319419206;9783319419190
this paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to disambiguate wrongly retrieved cases in Case-Based Reasoning (CBR). CBRAR use class association rules (CARs) to generate an optimum FP-tree which holds a value of each node. the possible advantage offered is that more efficient results can be gained when SBR returns uncertain answers. We compare the CBR Query as a pattern with FP-CAR patterns to identify the longest length of the voted class. If the patterns are matched, the proposed strategy can select not just the most similar case but the correct one. Our experimental evaluation on real data from the UCI repository indicates that the proposed CBRAR is a better approach when compared to the accuracy of the CBR systems used in our experiments.
Fuzzy pattern classifiers are a recent type of classifiers making use of fuzzy membership functions and fuzzy aggregation rules, providing a simple yet robust classifier model. the fuzzy pattern classifier is parametr...
详细信息
ISBN:
(纸本)9783319210247;9783319210230
Fuzzy pattern classifiers are a recent type of classifiers making use of fuzzy membership functions and fuzzy aggregation rules, providing a simple yet robust classifier model. the fuzzy pattern classifier is parametric giving the choice of fuzzy membership function and fuzzy aggregation operator. Several methods for estimation of appropriate fuzzy membership functions and fuzzy aggregation operators have been suggested, but considering only fuzzy membership functions with symmetric shapes found by heuristically selecting a "middle" point from the learning examples. Here, an approach for learningthe fuzzy membership functions and the fuzzy aggregation operator from data is proposed, using a genetic algorithm for search. the method is experimentally evaluated on a sample of several public datasets, and performance is found to be significantly better than existing fuzzy pattern classifier methods. this is despite the simplicity of the fuzzy pattern classifier model, which makes it interesting.
the objective of this paper is to identify the relationship between corporate governance variables and firm performance by employing datamining methods. We choose two dependent variables, Tobin's Q ratio and Altm...
详细信息
ISBN:
(纸本)9783319210247;9783319210230
the objective of this paper is to identify the relationship between corporate governance variables and firm performance by employing datamining methods. We choose two dependent variables, Tobin's Q ratio and Altman Z-score, as measures for the companies' performances and apply machinelearning techniques on the data collected from the components companies of three major stock indexes: S&P 500, STOXX Europe 600 and STOXX Eastern Europe 300. We use decision trees and logistic regressions as learning algorithms, and then we compare their performances. For the US components, we found a positive connection between the presence of women in the board and the company performance, while in Western Europe that it is better to employ a larger audit committee in order to lower the bankruptcy risk. An independent chairperson is a positive factor related to Altman Z-score, for the companies from Eastern Europe.
暂无评论