Feature selection based on a normalized difference measure for text classification

Feature selection based on a normalized difference measure for text classification

Feature selection based on a normalized difference measure for text classification

 

a b s t r a c t

The goal of feature selection in text classification is to choose highly distinguishing fea- tures for improving the performance of a classifier. The well-known text classification fea- ture selection metric named balanced accuracy measure (ACC2) (Forman, 2003) evaluates a term by taking the difference of its document frequency in the positive class (also known as true positives) and its document frequency in the negative class (also known as false positives). This however results in assigning equal ranks to terms having equal difference, ignoring their relative document frequencies in the classes. In this paper we propose a new feature ranking (FR) metric, called normalized difference measure (NDM), which takes into account the relative document frequencies. The performance of NDM is investigated against seven well known feature ranking metrics including odds ratio (OR), chi squared (CHI), information gain (IG), distinguishing feature selector (DFS), gini index (GINI) ,bal- anced accuracy measure (ACC2) and Poisson ratio (POIS) on seven datasets namely We- bACE(WAP,K1a,K1b), Reuters (RE0, RE1),spam email dataset and 20 newsgroups using the multinomial naive Bayes (MNB) and supports vector machines (SVM) classifiers. Our re- sults show that the NDM metric outperforms the seven metrics in 66% cases in terms of macro-F1 measure and in 51% cases in terms of micro F1 measure in our experimental trials on these datasets.

 

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Classification of text documents based on score level fusion approach

Classification of text documents based on score level fusion approach

Classification of text documents based on score level fusion approach

 

a b s t r a c t

Text document classification is a well known theme in the field of the information retrieval and text min- ing. Selection of most desired features in the text document plays a vital role in classification problem. This research article addresses the problem of text classification by considering Sentence–Vector Space Model (S-VSM) and Unigram representation models for the text document. An enhanced S-VSM model will be considered for the constructive representation of text documents. A neural network based rep- resentation for text documents is proposed for effective capturing of semantic information of the text data. Two different classifiers are designed based on the two different representation models of the text documents. Score level fusion is applied on two proposed models to find out the overall accuracy of the proposed model. Key contributions of the paper are an enhanced S-VSM model, an interval valued rep- resentation model for the proposed S-VSM approach. A word level representation model for semantic information preserving of the text document and score level fusion approach.

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A feature selection model based on genetic rank aggregation for text sentiment classification

A feature selection model based on genetic rank aggregation for text sentiment classification

A feature selection model based on genetic rank aggregation for text sentiment classification

 

Abstract

Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification.

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Automatic Cross-Language Retrieval Using Latent Semantic Indexing

Automatic Cross-Language Retrieval Using Latent Semantic Indexing

Automatic Cross-Language Retrieval Using Latent Semantic Indexing

 

Abstract

We describe a method for fully automated cross-language documenret trieval in whichn o queryt ranslation is required. Queriesi n one languagec an retrieve documenitns                    other languages (as well as the original language). This is accomplished by a methodth at automaticallyc onstructs a multilingual semantic space using Latent Semantic                          Indexing (LSI). Strong test results for the cross-languageL SI( CLLSI) methoda re presentedf or a newF rench-Englishco llection. Wea lso provide evidencet hat this                                automaticm ethod performsc omparabltyo a retrieval methodb ased on machine translation (MT-LSIa),n d explores everal practical training methods.B y all available                            measures,C L-LSpI erformsq uite well and is widelya pplicable.

 

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TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL

TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL

TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL

 

Abstract-

The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms
produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective term weighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.

 

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Machine Learning in Automated Text Categorization

Machine Learning in Automated Text Categorization

Machine Learning in Automated Text Categorization

Abstract:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of
documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning
techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

 

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Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Abstract:

5G cellular networks are assumed to be the key enabler and infrastructure provider in the ICT industry, by offering a variety of services with diverse requirements. The standardization of  5G cellular networks is being expedited, which also implies more of the candidate technologies will be adopted. Therefore, it is worthwhile to provide insight into the candidate techniques as a whole and examine the design philosophy behind them. In this article, we try to highlight one of the most fundamental features among the revolutionary techniques in the 5G era, i.e., there emerges initial intelligence in nearly every important aspect of cellular networks, including radio resource management, mobility management, service provisioning management, and so on. However, faced with ever-increasingly complicated configuration issues and blossoming new service requirements, it is still insufficient for 5G cellular nIntelligent 5G: When Cellular Networks Meet Artificial IntelligenetNetworks if it lacks complete AI functionalities. Hence, we further introduce fundamental concepts in AI and discuss the relationship between AI and the candidate techniques in 5G cellular networks. Specifically, we highlight the opportunities and challenges to exploit AI to achieve intelligent 5G networks, and demonstrate the effectiveness of AI to manage and orchestrate cellular network resources. We envision that AI-empowered 5G cellular networks will make the acclaimed ICT enabler a reality.

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Artificial intelligence projects using matlab in iran

Artificial intelligence projects using matlab in iran

Artificial intelligence projects using matlab in iran

Artificial intelligence (AI, also machine intelligenceMI) is apparently intelligent behaviour by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic gamesystems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. However, in the early 21st century statistical approaches to machine learning became successful enough to eclipse all other tools, approaches, problems and schools of thought.

The traditional problems (or goals) of AI research include reasoningknowledgeplanninglearningnatural language processingperception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methodscomputational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimizationneural networks and methods based on statistics, probability and economics. The AI field draws upon computer sciencemathematicspsychologylinguisticsphilosophyneuroscienceartificial psychology and many others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by mythfiction and philosophy since antiquity. Some people also consider AI a danger to humanity if it progresses unabatedly.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.

 

Artificial intelligence projects using matlab in iran


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Projects related Keywords:-

Solar, Wind, Renewable Energy, Maximum Power Point Tracking (MPPT), Load Flow, Economic-Dispatch, Unit Commitment, Distribution Network Reconfiguration, Transmission System, FACT Analysis, Power Market Analysis, Ancillary Services, Smart Grid, Hybrid, Integration, Power Loss Minimization, Power Market Cost Optimization, Generation, Reactive Power Procurement Model, Neural Network, Fuzzy Logic, Neuro-Fuzzy, Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Firefly Optimization, Fireworks Optimization, Backward-Forward Sweep, Fuzzy Tuned Methods, MATLAB, Simulink, Power System Analysis Tool (PSAT), General Algebraic Modeling System (GAMS).


Projects Related Queries:-

power systems, power lead system, solar power systems, economic dispatch, economic dispatch in power system, economic load dispatch using genetic algorithm, power system analysis, power system operation and control, power system analysis and design, power system control and stability, power system dynamics stability and control, power distribution system development project, power system projects using matlab.

Artificial intelligence projects using matlab in Qatar

Artificial intelligence projects using matlab in Qatar

Artificial intelligence projects using matlab in Qatar

Artificial intelligence (AI, also machine intelligenceMI) is apparently intelligent behaviour by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic gamesystems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. However, in the early 21st century statistical approaches to machine learning became successful enough to eclipse all other tools, approaches, problems and schools of thought.

The traditional problems (or goals) of AI research include reasoningknowledgeplanninglearningnatural language processingperception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methodscomputational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimizationneural networks and methods based on statistics, probability and economics. The AI field draws upon computer sciencemathematicspsychologylinguisticsphilosophyneuroscienceartificial psychology and many others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by mythfiction and philosophy since antiquity. Some people also consider AI a danger to humanity if it progresses unabatedly.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.

 

Artificial intelligence projects using matlab in Qatar


What we provide:

  • Complete Research Assistance

Technology Involved:-

  • MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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  • Complete Code of this paper
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  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work

 


Projects related Keywords:-

Solar, Wind, Renewable Energy, Maximum Power Point Tracking (MPPT), Load Flow, Economic-Dispatch, Unit Commitment, Distribution Network Reconfiguration, Transmission System, FACT Analysis, Power Market Analysis, Ancillary Services, Smart Grid, Hybrid, Integration, Power Loss Minimization, Power Market Cost Optimization, Generation, Reactive Power Procurement Model, Neural Network, Fuzzy Logic, Neuro-Fuzzy, Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Firefly Optimization, Fireworks Optimization, Backward-Forward Sweep, Fuzzy Tuned Methods, MATLAB, Simulink, Power System Analysis Tool (PSAT), General Algebraic Modeling System (GAMS).


Projects Related Queries:-

power systems, power lead system, solar power systems, economic dispatch, economic dispatch in power system, economic load dispatch using genetic algorithm, power system analysis, power system operation and control, power system analysis and design, power system control and stability, power system dynamics stability and control, power distribution system development project, power system projects using matlab.