A Combined Approach for Filter Feature Selection in Document Classification

Nam, Le Nguyen Hoai; Quoc, Ho Bao

doi:10.1109/ictai.2015.56

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…The feature filtering methods used in the study are document frequency (DF) together with term variance (TV). To exploit the advantages of two different FS methods, a hybridization of cluster-based and the frequency-based approach is presented by Nguyen and Bao [13]. The proposed method termed FCFS on comparison with its counterpart achieved the best performance in terms of micro-F1.…”

Section: Related Workmentioning

confidence: 99%

“…One of the most crucial steps of the preprocessing of text data is the presentation of text documents into vector space via Bag_of_Word (BOW) [13][14] [15]. The final product of this task is associated with two main issues, a vast number of features representation, and the presence of irrelevant and noisy features which general termed high dimensionality [15].…”

Section: Introductionmentioning

confidence: 99%

“…Information Gain [38], Chi-Square [39], Fisher Score [40], ReliefF [41], t-test [4] are among the few filter based methods. Wrapper methods are dependent on classifiers that they frequently interact with the classification algorithm in order to construct a subset of informative features [13][35] [27]. They evaluate a particular feature subset by training and testing a given classifier.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Feature Filtering Approach by Integrating IG and T-Test Evaluation Metrics for Text Classification

Ado¹,

Deris²,

Samsudin³

2021

IJACSA

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High dimensionality is one of the main issues associated with text classification, such as selecting the most discrepant features subset for classifier's effective utilization is a difficult task. This significant preprocessing stage of selecting the relevant features is often called feature selection or feature filtering. Eliminating the non-relevant and noise features from the original feature set will drastically reduce the size of the feature set and the time complexity of the classification models and also improve or maintain their performance. Most of the existing filtering method produced a subset with relatively high number of features without much significant impact on running time, or produced subset with lesser number of features but results in performance degradation. In this paper, we proposed a new bi-strategy filtering approach that integrates Information Gain with t-test that selects a subset of informative features by considering both the score and ranking of respective features. Our approach considers the results' disparity produced by the benchmark metrics used in order to maximized and lessen their advantage and disadvantage. The approach set a new threshold parameter by computing V-score of the features with minimum scores present in both the two subsets and further refined the selected features. Hence, it reduces the size of the features subset without losing much informative features. Experiment results conducted on three different text datasets have shown that the proposed method is able to select features that are highly discrepant and at the same time achieves a significant improvement in terms of classification accuracy and F-score at the cost of a minimum running time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Feature Filtering Approach by Integrating IG and T-Test Evaluation Metrics for Text Classification

Ado¹,

Deris²,

Samsudin³

2021

IJACSA

View full text Add to dashboard Cite

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“…The Naive Bayes model is more efficient than that of the logistic regression scheme, nearest neighbour, decision tree and neural network, according to Receiver Operating Characteristic (ROC) curve, which is more significantly implemented in the research domain. The model is a simple, less parameterized and efficient one in terms of performance [11]. KNN is a special type of instance-based classification model, in which approximation function is computed locally and it continues until classification occurs.…”

Section: Related Workmentioning

confidence: 99%

An novel cluster based feature selection and document classification model on high dimension trec data

Kumari¹,

Satyanarayana²

2017

IJET

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TREC text documents are complex to analyze the features its relevant similar documents using the traditional document similarity measures. As the size of the TREC repository is increasing, finding relevant clustered documents from a large collection of unstructured documents is a challenging task. Traditional document similarity and classification models are implemented on homogeneous TREC data to find essential features for document entities that are similar to the TREC documents. Also, most of the traditional models are applicable to limited text document sets for text analysis. The main issues in the traditional text mining models in TREC repository include :1) Each document is represented in vector form with many sparsity values 2) Failed to find the document semantic similarity between the intra and inter clusters 3) High mean squared error rate. In this paper, novel feature selection based clustered and classification model is proposed on large number of different TREC repositories. Traditional latent Semantic Indexing and document clustering models are failed to find the topic relevance on large number of TREC clinical text document sets due to computational memory and time. Proposed document feature selection and clustered based classification model is applied on TREC clinical benchmark datasets. From the experimental results, it is proved that the proposed model is efficient than the existing models in terms of computational memory, accuracy and error rate are concerned.

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“…Many strategies for classifying research articles have been presented in the literature. These techniques are characterized as citation, metadata, content-based, or hybrid techniques [2], [3], [4], [5], [6]. Metadata based approaches are important among them due to its nature (always free available online).…”

Section: Introductionmentioning

confidence: 99%