scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2007 ◽  
pp. 175-208 ◽  
Author(s):  
Alex Freitas ◽  
André Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2011 ◽  
pp. 114-140
Author(s):  
Alex Freitas ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2008 ◽  
pp. 119-145
Author(s):  
Alex Freitas ◽  
Andre´ C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

Classification Techniques in Data Mining

2017 ◽  
pp. 153-188
Author(s):  
Ali Hosseinzadeh ◽  
S. A. Edalatpanah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Classification ◽  
Classification Techniques ◽  
Learning Machines ◽  
Advantages And Disadvantages ◽  
Behavior Based ◽  
Knowledge Learning

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

Interactive Classification Using a Granule Network

2011 ◽  
pp. 235-245
Author(s):  
Yan Zhao ◽  
Yiyu Yao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Classification System ◽  
Search Space ◽  
Classification Methods ◽  
Classification Problems ◽  
Computing Model ◽  
Tree Construction ◽  
Learning Data

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This chapter studies interactive classification at 3 levels. At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing model is suggested for re-examining not only existing classification problems, but also interactive classification problems. At the application level, an interactive classification system (ICS), using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction, and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.

Predictive Data Mining

2011 ◽  
pp. 3105-3110 ◽  
Author(s):  
Sotiris Kotsiantis ◽  
Panayotis Pintelas
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Programs ◽  
Classification Problems ◽  
Data Set ◽  
Future Data ◽  
Regression Problems ◽  
Discrete Values ◽  
Predictive Data Mining ◽  
Technical Basis

Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning (ML) provides the technical basis of data mining. It is used to extract information from the raw data in databases—information that is expressed in a comprehensible form and can be used for a variety of purposes. Every instance in any data set used by ML algorithms is represented using the same set of features. The features may be continuous, categorical, or binary. If instances are given with known labels (the corresponding correct outputs), then the learning is called supervised in contrast to unsupervised learning, where instances are unlabeled (Kotsiantis & Pintelas, 2004). This work is concerned with regression problems in which the output of instances admits real values instead of discrete values in classification problems.

scholarly journals Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

2017 ◽  
Vol 7 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Andri Riid ◽  
Jürgo-Sören Preden
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Rule ◽  
Complexity Reduction ◽  
Error Reduction ◽  
Classification Systems ◽  
Classification Problems ◽  
Rule Based ◽  
Classification Approach ◽  
Good Balance

AbstractThis paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

scholarly journals Hierarchical Classification Using Binary Data

AI Magazine ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 59-65
Author(s):  
Denali Molitor ◽  
Deanna Needell
Keyword(s):  
Hierarchical Structure ◽  
Binary Data ◽  
Hierarchical Classification ◽  
Hierarchical Data ◽  
Classification Problems ◽  
Classification Approach ◽  
Hierarchical Relationship ◽  
Class Labels ◽  
Number Of Classes

In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we show case computational and accuracy advantages.

scholarly journals Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

2021 ◽  
Author(s):  
Neeraj Kumar ◽  
Upendra Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Dimensionality Reduction ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Classification Techniques ◽  
Network Intrusion ◽  
Depth Analysis

Abstract Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

Performance Implications of Knowledge Discovery Techniques in Databases

2011 ◽  
pp. 191-212
Author(s):  
Balaji Rajagopalan ◽  
Ravindra Krovi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Business Decision ◽  
Effective Implementation ◽  
Simulation Based ◽  
Careful Assessment ◽  
Mining Tools

This chapter introduces knowledge discovery techniques as a means of identifying critical trends and patterns for business decision support. It suggests that effective implementation of these techniques requires a careful assessment of the various data mining tools and algorithms available. Both statistical and machine-learning based algorithms have been widely applied to discover knowledge from data. In this chapter we describe some of these algorithms and investigate their relative performance for classification problems. Simulation based results support the proposition that machine-learning algorithms outperform their statistical counterparts, albeit only under certain conditions. Further, the authors hope that the discussion on performance related issues will foster a better understanding of the application and appropriateness of knowledge discovery techniques.

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