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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Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

 

Abstract—

The current paper presents a novel texture-based method for detectingtexts in images. A support vector machine (SVM) is used to analyze the texturalproperties of texts. No external texture feature extraction module is used; rather,the intensities of the raw pixels that make up the textural pattern are fed directly tothe SVM, which works well even in high-dimensional spaces. Next, text regions areidentified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) tothe results of the texture analysis. The combination of CAMSHIFT and SVM sproduces both robust and efficient text detection, as time-consuming textureanalyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed.

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Machine learning an alternate technique to estimate the state of charge of energy storage devices

Machine learning an alternate technique to estimate the state of charge of energy storage devices

Machine learning an alternate technique to estimate the state of charge of energy storage devices

Abstract:

State of charge (SOC) estimation plays a critical role in the operation of an electric vehicle (EV) power battery. In this Letter, the authors propose machine learning (ML) algorithms as alternate to the existing filtering algorithms used for SOC estimation of an EV battery. The SOC estimation approach is evaluated by the simulation experiments in advanced vehicle simulator (ADVISOR). For the modelling of ML algorithms, the input parameters that affect the SOC estimation are battery current, battery module temperature, power out of the battery (available and requested), battery power loss and heat removed from the battery. Training and testing stages of the models are carried out using the data collected from ADVISOR. As the drive cycle conditions provided by ADVISOR are universal therefore present method is applicable to all kinds of batteries used in EVs including lithium ion, nickel metal hydride and lead acid batteries. Thus, the proposed models for SOC estimation provide an alternative approach in SOC estimation.
Machine learning an alternate technique to estimate the state of charge of energy storage devices
Published in: Electronics Letters Volume: 53Issue: 25, 12 7 2017 )
Page(s): 1665 – 1666
Date of Publication: 12 7 2017
Print ISSN: 0013-5194
INSPEC Accession Number: 17377750
Publisher: IET

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