Feature selection in machine learning: A new perspective

Cai, Jie; Luo, Juan; Wang, Shulin; Yang, Shuyuan

doi:10.1016/j.neucom.2017.11.077

Cited by 1,542 publications

(711 citation statements)

References 91 publications

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“…Reliability of hashtags and pragmatic features had to be ensured, and the accuracy was about 72.36%. Cai et al [19] developed a framework based on ensemble text feature selection method for detecting sarcasm, irony, and satire from reviews and news articles. e logistic regression method was found to achieve higher accuracy values of 91.46% and 88.86% in detection of satiric and ironic reviews, respectively.…”

Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

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Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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“…In general, the grouping-based method classifies the selecting features by taking three main stages [11,12] as described in Fig. 1 [13]. Those are: designing the structure of a feature space obtained from the domain of the distance of each feature; grouping features based on a clustering method; and the representing features of each cluster, which are selected to produce the selection results.…”

Section: Related Workmentioning

confidence: 99%

Cluster Analysis-Based Approach Features Selection on Machine Learning for Detecting Intrusion

Aziz¹,

Ahmad²

2019

IJIES

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Various machine learning technology approaches have been applied to intrusion detection system (IDS). To get optimal results, it needs to take several stages for processing the traffics. Among them is the feature selection method, where irrelevant and redundant features are removed. In the previous research, the system is developed based on feature grouping that used a clustering approach as the evaluation criteria. In this research, we propose a method for improving the performance of machine learning with the feature selection approach based on feature clustering. We propose cluster based feature selection derived from the value of mutual information and Pearson correlation. The cluster hierarchy is used in forming filters that are used to create selected and reduced clusters. In developing the cluster hierarchy, single, complete, and average linkage method are used to determine the formation of the best feature clusters. The classification method with Support Vector Machine (SVM), Naïve Bayes, and J48 decision tree are applied to observe the performance of the proposed feature selection. Based on the experimental results, we find that the highest accuracy (i.e., 99.842%) is obtained when a single linkage in the J48 classification is implemented in the Kyoto 2006 dataset.

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“…whereκ z is defined in (7) and T 1 is defined in (4).κ * adopts an entropy estimator with an exponentially decaying bias to improve the performance in estimating κ * and capturing the associations when the sample size is not sufficiently large. Furthermore, we expect involving the sample coverage would separate and drop situation 2 and 3 features under small samples.…”

Section: Estimationmentioning

confidence: 99%

CASMI—An Entropic Feature Selection Method in Turing’s Perspective

Shi

Zhang

2019

Entropy

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Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small.

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Feature selection in machine learning: A new perspective

Cited by 1,542 publications

References 91 publications

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Cluster Analysis-Based Approach Features Selection on Machine Learning for Detecting Intrusion

CASMI—An Entropic Feature Selection Method in Turing’s Perspective

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