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Data filter function incremental mining based on feature selection in an active distribution network

IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS 2020年第3期5卷 301-309页

作者： Deng, Song Cai, Qingyuan Zhang, Zi Yang, Lechan Huang, Tinglei Yuan, Changan Nanjing Univ Posts & Telecommun Nanjing Peoples R China Guilin Univ Elect Technol Guilin Peoples R China Jinling Inst Technol Nanjing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Guangxi Acad Sci Nanning Peoples R China

Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distribution network will bring great security challenges to data transmission. The existing data security methods, such as access control and encryption, address the security of massive, high dimensional, and non-text data in the active distribution network. Therefore, feature selection algorithm based on rough set is first given to reduce the complexity of massive and high dimensional data. And then, based on feature selection, the authors propose a data filtering function model mining algorithm by using gene expression programming (DFFM-FSGEP). Finally, to solve the data filter function model mining of the incremental dataset, they also present an incremental mining algorithm of the filtering function model based on functional fitting (IMFFM-FF). Experimental results show that the proposed algorithm in this study can greatly reduce the complexity of experimental datasets to be processed, and compared with the other algorithms, DFFM-FSGEP has higher classification accuracy and sensitivity, and IMFFM-FF has higher classification speed and classification accuracy for incremental datasets.

关键词： data mining security of data massive data high dimensional data data filtering function model function model mining incremental dataset incremental mining algorithm data filter function incremental mining active distribution network traditional distributed networks complex access environment flexible access mode massive access terminal great security challenges data transmission existing data security methods access control nontext data feature selection algorithm

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Effective Classification algorithms and feature selection for Bio-Medical Data using IoT 7

Effective Classification Algorithms and Feature Selection fo...

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7th International Conference on Information Technology Trends (ITT)

作者： Masood, Junaid Shahzad, Muhammad Khan, Zahoor Ali Akre, Vishesh Rajan, Amala Ahmed, Sheeran Masood, Fawad Iqra Natl Univ Peshawar Peshawar Pakistan MNS Univ Engn & Technol Multan Pakistan Career Dynam Res Ctr Peshawar Pakistan Higher Coll Technol CIS Fujairah U Arab Emirates Higher Coll Technol Dubai U Arab Emirates Inst Space Technol Islamabad Pakistan

ISBN: (纸本)9781728183787

Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas. The method of extracting usable data from larger set of arbitrary raw data is called data mining. This includes the analysis of sample data in large amounts of data with one or more programs. Concentrate on large amounts of data and databases to analyze, we use predictive analysis in the field of medicine. In this study, we examine the performance of algorithms alongwith the use of IoT that analyses large amounts of scattered information to make sense of it and turn it into knowledge. For this we used feature selection and classification algorithm on hepatitis data to get best result with minimum error rate and compare feature selection with classification algorithm so that to check which method is best according to results.

关键词： Data Mining Classification algorithm IoT feature selection algorithm Error rate

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A Novel feature selection Method Based on an Integrated Data Envelopment Analysis and Entropy Model

A Novel Feature Selection Method Based on an Integrated Data...

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2nd International Conference on Information Technology and Quantitative Management (ITQM)

作者： Bamakan, Seyed Mojtaba Hosseini Gholami, Peyman Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100090 Peoples R China Mellat Bank Iran TABM Dept Collect Co Tehran Iran

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories;Classification, feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCI's datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms. (C) 2014 Published by Elsevier B.V. Open access under CC BY-NC-ND license.

关键词： Data mining feature selection algorithm Entropy Data Envelopment Analysis Classification

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feature selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies

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GLOBAL BUSINESS REVIEW 2024年第2期25卷 323-348页

作者： Gupta, Sonika Mehta, Sushil Kumar Shri Mata Vaishno Devi Univ Sch Comp Sci & Engn Katra 182320 Jammu & Kashmir India Shri Mata Vaishno Devi Univ Sch Business Katra Jammu & Kashmir India

The financial fraud detection problem involves analysis of the large financial datasets. Financial statement fraud detection process is concentrated on two major aspects: first, identification of the financial variables and ratios, also termed as features. Second, applying the data mining methods to classify the organizations into two broad categories: fraudulent and non-fraudulent organizations. If the input dataset contains large number of irrelevant and correlated features, the computational load of the machine learning technique increases and the effectiveness of the classification outcomes decreases. The feature selection process selects a subset of most significant attributes or variables that can be the representative of original data. This selected subset can help in learning the pattern in data at much less time and with accuracy, in order to produce useful information for decision-making. This article briefly states the methods applied in the prior studies for selecting the features for financial statement fraud detection. This article also presents an approach to feature selection using correlation-based filter selection methods in which feature selection is performed based on ensemble model, and tests the outcome of the approach by applying the mean ratio analysis on financial data of Indian companies.

关键词： Financial statement frauds feature selection process correlation-based filter feature selection methods feature selection algorithm mean ratio analysis

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Extensive Survey on feature Extraction and feature selection Techniques for Sentiment Classification in Social Media 10

Extensive Survey on Feature Extraction and Feature Selection...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Kumar, S. Sathish Rajini, Aruchamy Hindustan Coll Arts & Sci Dept Comp Sci Coimbatore 641028 Tamil Nadu India NGM Coll Dept Comp Sci Pollachi 624001 Tamil Nadu India

ISBN: (纸本)9781538659069

Data Mining is a process ofgenerating new information from the existing datasets involving the machine learning, statistics and database systems. Major role of data mining is to turn raw data into useful information by using mathematical algorithms for predicting future events. Data mining is known as knowledge discovery in data (KDD). The key properties of data mining are automatic discovery of patterns, prediction of likely outcomes, creation of actionable information and focuses on large datasets and database. Mining the information related to a content-oriented extraction on the basics offeature of data. For accessing the features of data that is data retrieval can be done by feature extraction mechanism. Various feature extraction methods have been used along with a feature selection algorithmfor mining the large datasets.

关键词： Knowledge Discovery feature selection algorithm Data Retrieval

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feature selection environment for genomic applications

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BMC BIOINFORMATICS 2008年第1期9卷 451-451页

作者： Lopes, Fabricio Martins Martins, David Correa, Jr. Cesar, Roberto M., Jr. Univ Sao Paulo Inst Matemat & Estat BR-05508090 Sao Paulo Brazil Univ Tecnol Fed Parana COINF BR-86300000 Cornelio Procopio PR Brazil

Background: feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer;or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

关键词： feature selection Criterion Function feature selection Method Conditional Entropy feature selection algorithm

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A Novel feature selection Method based on an Integrated Data Envelopment Analysis and Entropy Model

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Procedia Computer Science 2014年 31卷 632-638页

作者： Seyed Mojtaba Hosseini Bamakan Peyman Gholami Research Center of Fictitious Economy & Data Science University of Chinese Academy of Sciences Beijing 100090 China TABM (Dept Collection Company Affiliated to Mellat Bank of Iran)

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories; Classification, feature selection, Assassination rules and Clustering. One of the most important of these functions is feature selection which has been increasingly developed and many researchers provide variety of algorithms to deal with this function in recent years. feature selection algorithms mostly used for obtaining more precise and strong machine learning algorithms along with reducing the computation time. Another growing science is Multiple Criteria Decision Making techniques witch it also has variety of methods. In this paper, we use both Data Envelopment Analysis which is a useful technique for determining the efficiency of decision-making units and Entropy method which its function is weighting the criteria to selecting the appropriate features. Hence, our novel integrated method has been analyzed by implementing in a testing environment and we apply it on three datasets of UCI's datasets, so the result showed our innovated approach has comparable accuracy with the other feature selections algorithms.

关键词： Data mining feature selection algorithm Entropy Data Envelopment Analysis Classification

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Multi-task feature selection in microarray data by binary integer programming

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BMC proceedings 2013年第Suppl 7期7卷 S5页

作者： Liang Lan Slobodan Vucetic 9. Department of Computer and Information Science Temple University Philadelphia PA USA

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

关键词： feature selection feature Subset feature selection Method feature selection algorithm Quadratic Programming

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Modified Filter Based feature selection Technique for Dermatology Dataset Using Beetle Swarm Optimization

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第2期10卷

作者： Rajeshwari, J. Sughasiny, M. Srimad Andavan Arts & Sci Coll Dept Comp Sci Tiruchirappalli Tamil Nadu India

INTRODUCTION: Skin cancer is an emerging disease all over the world which causes a huge mortality. To detect skin cancer at an early stage, computer aided systems is designed. The most crucial step in it is the feature selection process because of its greater impact on classification performance. Various feature selection algorithms were designed previously to find the relevant features from a set of attributes. Yet, there arise challenges in selecting appropriate features from datasets related to disease prediction. OBJECTIVES: To design a hybrid feature selection algorithm for selecting relevant feature subspace from dermatology datasets. METHODS: The hybrid feature selection algorithm is designed by integrating the Latent Semantic Index (LSI) along with correlation-based feature selection (CFS). To achieve an optimal selection of feature subset, beetle swarm optimization is used. RESULTS: Statistical metrics such as accuracy, specificity, recall, F1 score and MCC are calculated. CONCLUSION: The accuracy and sensitivity value obtained is 95% and 92%.

关键词： Skin cancer feature selection algorithm LSI CFS Beetle swarm optimization Classification performance

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Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data

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ENERGY CONVERSION AND MANAGEMENT 2024年 305卷

作者： Ehteram, Mohammad Nia, Mahdie Afshari Panahi, Fatemeh Farrokhi, Alireza Semnan Univ Dept Water Engn Semnan Iran Univ Kashan Fac Nat Resources & Earth Sci Kashan Iran Alaodoleh Semnani Inst Higher Educ ASIHE Dept Civil & Environm Engn Garmsar Iran

The prediction of solar radiation data is important for countries to reduce their dependence on fossil fuels. Since the development of solar energy systems relies on an accurate prediction of solar radiation data, this study is conducted to predict monthly and daily solar radiation data and contribute to the development of solar energy systems. The current study develops a long short term memory (LSTM) model that can extract temporal features more efficiently than other deep learning models and predict solar radiation data. The new model is called the Read-first LSTM (RLSTM) model. The gate units of the LSTM model are independent, so they may not fully extract the features of long time series. Thus, the current study is conducted to address the limitations of the LSTM model for predicting solar data. The main innovation of this study is to develop an improved LSTM model to predict solar radiation data and establish a collaborative process between gates. While recent studies focus on optimizing LSTM parameters, the current research improves the efficiency of LSTM gates. Since there is a collaborative process between the gates of the RLSTM, correlation values , and temporal features can be captured effectively. Climate data are used to predict solar radiation in two basins of Iran country, including the Kashan Plain and the Sefidorod Basin. The Boruta-Random Forest (BRF) feature selection algorithm was used to determine the best input scenario. The RLSTM model was compared with the LSTM model, recurrent neural network (RNN), radial basis function neural network (RBFNN), and Bidirectional LSTM (BILSTM) model. The RLSTM model could successfully predict the monthly solar radiation data in the Kashan plain. The RLSTM decreased the testing mean absolute error (MAE) of the other models by 5.8%-42%, respectively. The RLSTM model also accurately predicted daily data in the Sefidrood basin. The RLSTM improved the testing index of agreement (IA) of the BILSTM, LSTM, RNN, and RB

关键词： Long short term memory model Deep learning models feature selection algorithm Solar radiation data

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