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Author

Fumin Ma

Other affiliations: University College Dublin
Bio: Fumin Ma is an academic researcher from Nanjing University of Finance and Economics. The author has contributed to research in topics: Cluster analysis & Photovoltaic system. The author has an hindex of 7, co-authored 15 publications receiving 127 citations. Previous affiliations of Fumin Ma include University College Dublin.

Papers
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Journal ArticleDOI
TL;DR: An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper and the validity of this algorithm is demonstrated by simulation and experimental analysis.
Abstract: Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.

46 citations

Journal ArticleDOI
TL;DR: A PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy, and results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency.

35 citations

Journal ArticleDOI
TL;DR: To mitigate adverse effects of imbalanced clusters and decrease the computational cost, an interval type-2 fuzzy local measure for the RKM clustering is proposed, on the basis of which, a novel RKm clustering algorithm has been developed that specifically gives due consideration to im balanced clusters.
Abstract: Rough K-Means (RKM) is an efficient clustering algorithm for overlapping datasets, and has captured increasing attention in recent years. RKM algorithms are the main focus on the further description of uncertain objects located in boundary regions in order to improve the performance. However, most available RKM algorithms fail to pay attention to the influence of imbalanced clusters, together with imbalanced spatial distributions (i.e., the cluster density) and differing cluster sizes (i.e., the number of object ratios). This paper seeks to address this deficiency and examines in detail some adverse effects caused by imbalanced clusters. To mitigate adverse effects of imbalanced clusters and decrease the computational cost, an interval type-2 fuzzy local measure for the RKM clustering is proposed, on the basis of which, a novel RKM clustering algorithm has been developed that specifically gives due consideration to imbalanced clusters. The effectiveness and superiority of this algorithm are demonstrated through simulation and experimental analysis.

34 citations

Proceedings ArticleDOI
25 May 2013
TL;DR: A remote control system of smart appliances based on ZigBee wireless sensor network that can realize the remote control and inquiry of household appliances and has the advantages of convenient in control, flexible in adding new devices.
Abstract: Diversification of remote control mode is the inevitable trend of development of smart appliances. In this paper we designed and developed a remote control system of smart appliances based on ZigBee wireless sensor network, realizing the diversification of remote control mode of smart appliances. The ZigBee technology is used to form a control network of household appliances within the house, two remote control networks of Internet and SMS are set up with the network interface module and GSM module. Status of the home appliances can be queried and controlled through either the remote PC interface or mobile phones. The experimental results show that: the system is reliable and can realize the remote control and inquiry of household appliances. The system has the advantages of convenient in control, flexible in adding new devices.

18 citations

Journal ArticleDOI
TL;DR: An incremental attribute reduction algorithm for group dynamic data is developed and the single dynamic object and the group dynamic objects are both considered in this algorithm.

18 citations


Cited by
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Dissertation
01 Jul 2016
TL;DR: In this paper, a clustering-based under-sampling strategy was proposed to balance the imbalance between the minority class and the majority class, where the number of clusters in the majority classes is set to be equal to the number in the minority classes.
Abstract: Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

336 citations

Journal Article
TL;DR: A new extension of rough set based on limited tolerance relation is presented, which combines tolerance relation, non-symmetric similarity relation, and valued tolerance relation.
Abstract: The classical rough set theory developed by professor Pawlak is based on complete information systems. It classifies objects using upper-approximation and lower-approximation defined on an indiscernibility relation that is a kind of equivalent relation. In order to process incomplete information systems, the classical rough set theory needs to be extended, especially, the indiscernibility relation needs to be extended to some inequivalent relation. There are several extensions for the indiscernibility relation now, such as tolerance relation, non-symmetric similarity relation, and valued tolerance relation. Unfortunately, these extensions have their own limitation. Presented in this paper is a new extension of rough set based on limited tolerance relation. The performances of these extended rough set models are also compared.

115 citations

Journal ArticleDOI
TL;DR: This survey will directly help researchers understand the research developments of MSIF under RST and provide state-of-the-art understanding in specialized literature, as well as clarify the approaches and application of MSif in RST research community.

105 citations

Journal ArticleDOI
TL;DR: A novel discrete grey model with time-varying parameters is initially designed to deal with various PPG time series featured with nonlinearity, periodicity, and volatility, which widely exist in the long-term PPG sequences.

103 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that direct explainable neural network not only exhibits a better prediction performance than traditional neural networks such as support vector regression, but also mathematically interprets how the input of the forecasting model affects the final prediction results, showing that the proposed explainable Neural Network has a high application potential in the real world.

58 citations