The Experts below are selected from a list of 7140 Experts worldwide ranked by ideXlab platform
Elena Sapozhnikova - One of the best experts on this subject based on the ideXlab platform.
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Multi-label Classification and extracting predicted Class hierarchies
Pattern Recognition, 2011Co-Authors: Florian Brucker, Fernando Benites, Elena SapozhnikovaAbstract:This paper investigates hierarchy extraction from results of multi-label Classification (MC). MC deals with instances labeled by multiple Classes rather than just one, and the Classes are often hierarchically organized. Usually multi-label Classifiers rely on a Predefined Class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system Classifies multi-label data and extracts a Class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner.
Florian Brucker - One of the best experts on this subject based on the ideXlab platform.
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Multi-label Classification and extracting predicted Class hierarchies
Pattern Recognition, 2011Co-Authors: Florian Brucker, Fernando Benites, Elena SapozhnikovaAbstract:This paper investigates hierarchy extraction from results of multi-label Classification (MC). MC deals with instances labeled by multiple Classes rather than just one, and the Classes are often hierarchically organized. Usually multi-label Classifiers rely on a Predefined Class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system Classifies multi-label data and extracts a Class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner.
Fernando Benites - One of the best experts on this subject based on the ideXlab platform.
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Multi-label Classification and extracting predicted Class hierarchies
Pattern Recognition, 2011Co-Authors: Florian Brucker, Fernando Benites, Elena SapozhnikovaAbstract:This paper investigates hierarchy extraction from results of multi-label Classification (MC). MC deals with instances labeled by multiple Classes rather than just one, and the Classes are often hierarchically organized. Usually multi-label Classifiers rely on a Predefined Class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system Classifies multi-label data and extracts a Class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner.
Parisa Rashidi - One of the best experts on this subject based on the ideXlab platform.
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activity discovery and activity recognition a new partnership
IEEE Transactions on Systems Man and Cybernetics, 2013Co-Authors: Diane J Cook, Narayanan C Krishnan, Parisa RashidiAbstract:Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a Predefined Class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a Predefined Class aids in understanding this data and segmenting it into learnable Classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets.
Ali Baig - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of different data mining Classification algorithm and predictive analysis
IOSR Journal of Computer Engineering, 2013Co-Authors: Syeda Farha Shazmeen, Mirza Mustafa, Ali BaigAbstract:Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information; data mining has become an essential component in various fields of human life. It is used to identify hidden patterns in a large data set. Classification techniques are supervised learning techniques that Classify data item into Predefined Class label. It is one of the most useful techniques in data mining to build Classification models from an input data set; these techniques commonly build models that are used to predict future data trends. In this paper we have worked with different data mining applications and various Classification algorithms, these algorithms have been applied on different dataset to find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new Class labels.