Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis

Bruni, Renato; Bianchi, Gianpiero

doi:10.1109/tkde.2015.2416727

Cited by 31 publications

(25 citation statements)

References 27 publications

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“…Statistical and Logical Analysis of Data (SLAD) is a classification methodology based on Boolean logic and discrete optimization [18]. It constitutes a recent version of the classical approach of Logical Analysis of Data (LAD) [19] incorporating scoring criteria proposed in [20].…”

Section: The Classification Proceduresmentioning

confidence: 99%

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Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Bianchi

Bruni

Scalfati

2018

Mathematical Problems in Engineering

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Monitoring specific features of the enterprises, for example, the adoption of e-commerce, is an important and basic task for several economic activities. This type of information is usually obtained by means of surveys, which are costly due to the amount of personnel involved in the task. An automatic detection of this information would allow consistent savings. This can actually be performed by relying on computer engineering, since in general this information is publicly available on-line through the corporate websites. This work describes how to convert the detection of e-commerce into a supervised classification problem, where each record is obtained from the automatic analysis of one corporate website, and the class is the presence or the absence of e-commerce facilities. The automatic generation of similar data records requires the use of several Text Mining phases; in particular we compare six strategies based on the selection of best words and best n-grams. After this, we classify the obtained dataset by means of four classification algorithms: Support Vector Machines; Random Forest; Statistical and Logical Analysis of Data; Logistic Classifier. This turns out to be a difficult case of classification problem. However, after a careful design and set-up of the whole procedure, the results on a practical case of Italian enterprises are encouraging.

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Section: The Classification Proceduresmentioning

confidence: 99%

“…Finally, each unlabeled record is classified on the basis of the weighted sum of the patterns covering that record. We use the C++ implementation of this classifier that was used in the work [18].…”

Section: The Classification Proceduresmentioning

confidence: 99%

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Bianchi

Bruni

Scalfati

2018

Mathematical Problems in Engineering

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“…A number of researches have been done emphasizing on the work of P.L Hammer [3] [4] [8] and determining the accuracy of techniques for logical analysis of data [7]. However LAD can be applied to a number of data analysis applications; a significant amount of work has been done in the field of medical science and related disciplines.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Presently, a large number of additional works is ongoing in this area such as research work in NIH Clinical Center for Radiology and Imaging Sciences, the NIH Clinical Proteomic Applications Center, Semelweiss Medical University (Budapest) etc. so that LAD can be effectively used for the analysis of medical data One of the recent works in relation to Logical Analysis of data has been initiated by Renato Bruni and Gianpiero Bianchi [8]. The proposed work deals with the improvement of standard LAD algorithm through statistical considerations on data.…”

Section: Literature Reviewmentioning

confidence: 99%

Logical Analysis of Data-A Survey Paper

Chauhan¹,

Saxena²,

Tripathi³

2017

IJARCSSE

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Abstract-Logical analysis of data is a methodology used for analyzing observations and detecting structural information which provides a solution to problems such as classification, marketing, feature selection development of pattern-based decision support systems, detection of inconsistencies in databases etc. There are a number of areas such as statistics, clustering, machine learning etc that are parallel to problems evaluated by LAD. However, it plays a significant role in the field of data classification through systematic identification of 'patterns' in the datasets There are a large number of real world data analysis applications such as economics, oil exploration, medical diagnosis etc. that can be formulated using Logical analysis of data. Although, in the recent years medical and related disciplines have been the focus of LAD and there have been a considerable number of medical problems to which LAD is successfully applied. The goal of this paper is to provide an overview and a comparative study between different works related to Logical analysis of data (LAD) in the field of data classification. It gives an insight of how Logical analysis of data (LAD) has been successfully applied to various data analysis applications particularly medical science and related disciplines. It provides a brief description of some useful work done in the past in the field of data classification to provide an accurate data classification using a small training set. It particularly gives an overview about the work done by P.L Hammer and Renato Bruni and Gianpiero Bianchi in the field of data classification. It explores about the characteristics and challenges involved with the past research work and also contributes significant variations to them to provide a fast and accurate data classification.

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“…A special kind of discretization for data with many attributes was presented in [25]. Discretization combined with semi-supervised learning was presented in [3]. Many papers emphasizing the importance of discretization to data mining were recently published [13,17,18,28,29,31].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Four Approaches to Discretization Based on Entropy

Grzymała-Busse

Mroczek

2016

Entropy

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Abstract:We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four methods using two criteria: an error rate evaluated by ten-fold cross-validation and the size of the decision tree generated by C4.5. Our results show that multiple scanning is the best discretization method in terms of the error rate and that decision trees generated from datasets discretized by multiple scanning are simpler than decision trees generated directly by C4.5 or generated from datasets discretized by both globalized discretization methods.

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Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis

Cited by 31 publications

References 27 publications

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Logical Analysis of Data-A Survey Paper

A Comparison of Four Approaches to Discretization Based on Entropy

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