Classification Based on Unsupervised Learning

2011 ◽  
pp. 348-395
Author(s):  
Yu Wang
Keyword(s):  
Network Security ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Network Traffic ◽  
High Speed ◽  
Ad Hoc ◽  
Training Data ◽  
Traffic Data ◽  
Response Variable ◽  
Learning Techniques

The requirement for having a labeled response variable in training data from the supervised learning technique may not be satisfied in some situations: particularly, in dynamic, short-term, and ad-hoc wireless network access environments. Being able to conduct classification without a labeled response variable is an essential challenge to modern network security and intrusion detection. In this chapter we will discuss some unsupervised learning techniques including probability, similarity, and multidimensional models that can be applied in network security. These methods also provide a different angle to analyze network traffic data. For comprehensive knowledge on unsupervised learning techniques please refer to the machine learning references listed in the previous chapter; for their applications in network security see Carmines, Edward & McIver (1981), Lane & Brodley (1997), Herrero, Corchado, Gastaldo, Leoncini, Picasso & Zunino (2007), and Dhanalakshmi & Babu (2008). Unlike in supervised learning, where for each vector 1 2 ( , , , ) n X x x x = ? we have a corresponding observed response, Y, in unsupervised learning we only have X, and Y is not available either because we could not observe it or its frequency is too low to be fit ted with a supervised learning approach. Unsupervised learning has great meanings in practice because in many circumstances, available network traffic data may not include any anomalous events or known anomalous events (e.g., traffics collected from a newly constructed network system). While high-speed mobile wireless and ad-hoc network systems have become popular, the importance and need to develop new unsupervised learning methods that allow the modeling of network traffic data to use anomaly-free training data have significantly increased.

scholarly journals Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python

2020 ◽  
Vol 7 (2) ◽  
pp. 156
Author(s):  
Endang Retnoningsih ◽  
Rully Pramudita
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Training Data ◽  
Abstract Machine ◽  
Dbscan Clustering ◽  
Learning Techniques ◽  
Learning Technique ◽  
Learning Programming

Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bunga bermaca-macam warna dan memiliki sepal dan petal yang menunjukkan spesies bunga, dibutuhkan metode yang tepat untuk pengelompokan bunga-bunga tersebut kedalam spesiesnya iris-setosa, iris-versicolor atau iris-virginica. Penyelesaian digunakan Python yang menyediakan algoritma dan library yang digunakan membuat machine learning. Penyelesaian dengan teknik supervised learning dipilih algoritma KNN Clasiffier dan teknik unsupervised learning dipilih algoritma DBSCAN Clustering. Hasil yang diperoleh Python menyediakan library yang lengkap numPy, Pandas, matplotlib, sklearn untuk membuat pemrograman machine learning dengan algortima KNN memanggil from sklearn import neighbors termasuk teknik supervised, maupun DBSCAN memanggil from sklearn.cluster import DBSCAN termasuk teknik unsupervised learning. Kemampuan Python memberikan hasil output sesuai input dalam dataset menghasilkan keputusan berupa klasifikasi maupun klusterisasi.   Kata kunci: DBSCAN, KNN, machine learning, python.   Abstract: Machine learning is a system that is able to learn on its own to decide something without having to be repeatedly programmed by humans so that computers become smarter in learning from the experience of the data they have. Based on the learning technique, supervised learning can be distinguished using a dataset (training data) that is already labeled, while unsupervised learning draws conclusions based on the dataset. The input in the form of a dataset is used by machine learning to produce the correct analysis. The problem to be solved by iris flowers (iris tectorum), which has flowers of various colors and has sepals and petals that indicate the species of flowers, requires an appropriate method for grouping these flowers into iris-setosa, iris-versicolor or iris-virginica species. The solution is used by Python, which provides the algorithms and libraries used to make machine learning. The solution with the supervised learning technique was chosen by the KNN Clasiffier algorithm and the unsupervised learning technique was selected by the DBSCAN Clustering algorithm. The results obtained by Python provide a complete library of numPy, Pandas, matplotlib, sklearn to create machine learning programming with KNN algorithms calling from sklearn import neighbors including supervised techniques, and DBSCAN calling from sklearn.cluster import DBSCAN including unsupervised learning techniques. Python's ability to provide output according to the input in the dataset results in decisions in the form of classification and clustering.   Keywords: DBSCAN, KNN, machine learning, python.

Classification Methods

2011 ◽  
pp. 196-201 ◽  
Author(s):  
Aijun An
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Credit Card ◽  
A Priori ◽  
Training Data ◽  
Classification Model ◽  
Highly Active ◽  
Data Object ◽  
Patient Database ◽  
Data Objects

Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, distinguished from unsupervised learning. In supervised learning, the training data consists of pairs of input data (typically vectors), and desired outputs, while in unsupervised learning there is no a priori output. Classification has various applications, such as learning from a patient database to diagnose a disease based on the symptoms of a patient, analyzing credit card transactions to identify fraudulent transactions, automatic recognition of letters or digits based on handwriting samples, and distinguishing highly active compounds from inactive ones based on the structures of compounds for drug discovery.

Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

CLUSTERING-BASED NETWORK INTRUSION DETECTION

2007 ◽  
Vol 14 (02) ◽  
pp. 169-187 ◽  
Author(s):  
SHI ZHONG ◽  
TAGHI M. KHOSHGOFTAAR ◽  
NAEEM SELIYA
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Clustering Algorithms ◽  
Network Intrusion Detection ◽  
Learning Methods ◽  
High Detection Rate ◽  
Network Intrusion

Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised clustering algorithms for intrusion detection, and propose a simple yet effective self-labeling heuristic for detecting attack and normal clusters of network traffic audit data. The clustering algorithms investigated include, k-means, Mixture-Of-Spherical Gaussians, Self-Organizing Map, and Neural-Gas. The network traffic datasets provided by the DARPA 1998 offline intrusion detection project are used in our empirical investigation, which demonstrates the feasibility and promise of unsupervised learning methods for network intrusion detection. In addition, a comparative analysis shows the advantage of clustering-based methods over supervised classification techniques in identifying new or unseen attack types.

scholarly journals Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

2020 ◽  
Vol 32 (2) ◽  
Author(s):  
Oluwafemi Oriola ◽  
Eduan Kotzé
Keyword(s):  
Logistic Regression ◽  
Supervised Learning ◽  
South African ◽  
Learning Curves ◽  
Training Data ◽  
Support Vector ◽  
Learning Techniques ◽  
Learning Technique ◽  
Unlabelled Data ◽  
Language Detection

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have been skewed towards small amounts of labelled data, with small feature space. This paper, therefore, presents a semi-supervised learning technique that improves the distribution of training data by assigning labels to unlabelled data based on the majority voting over different feature sets of labelled and unlabelled data clusters. The technique is applied to South African English corpora consisting of labelled and unlabelled abusive tweets. The proposed technique is compared with state-of-the-art self-learning and active learning techniques based on syntactic and semantic features. The performance of these techniques with Logistic Regression, Support Vector Machine and Neural Networks are evaluated. The proposed technique, with accuracy and F1-score of 0.97 and 0.95, respectively, outperforms existing semi-supervised learning techniques. The learning curves show that the training data was used more efficiently by the proposed technique compared to existing techniques. Overall, n-gram syntactic features with a Logistic Regression classifier records the highest performance. The paper concludes that the proposed semi-supervised learning technique effectively detected implicit and explicit South African abusive language on Twitter.

scholarly journals Enhancing Image Diagnosis by the Implementation of Transfer Classifiers

2019 ◽  
Vol 8 (3) ◽  
pp. 999-1002
Keyword(s):  
Supervised Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Image Diagnosis ◽  
Sensitivity Problem ◽  
Common Application ◽  
Target Data

Images generated from a variety of sources and foundations today can pose difficulty for a user to interpret similarity in them or analyze them for further use because of their segmentation policies. This unconventionality can generate many errors, because of which the previously used traditional methodologies such as supervised learning techniques less resourceful, which requires huge quantity of labelled training data which mirrors the desired target data. This paper thus puts forward the mechanism of an alternative technique i.e. transfer learning to be used in image diagnosis so that efficiency and accuracy among images can be achieved. This type of mechanism deals with variation in the desired and actual data used for training and the outlier sensitivity, which ultimately enhances the predictions by giving better results in various areas, thus leaving the traditional methodologies behind. The following analysis further discusses about three types of transfer classifiers which can be applied using only small volume of training data sets and their contrast with the traditional method which requires huge quantities of training data having attributes with slight changes. The three different separators were compared amongst them and also together from the traditional methodology being used for a very common application used in our daily life. Also, commonly occurring problems such as the outlier sensitivity problem were taken into consideration and measures were taken to recognise and improvise them. On further research it was observed that the performance of transfer learning exceeds that of the conventional supervised learning approaches being used for small amount of characteristic training data provided reducing the stratification errors to a great extent

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

A NEURAL NETWORK THAT LEARNS FROM FUZZY DATA FOR LANGUAGE ACQUISITION

1996 ◽  
Vol 04 (06) ◽  
pp. 581-603
Author(s):  
CHIN-TENG LIN ◽  
MING-CHIH KAN ◽  
I-FANG CHUNG
Keyword(s):  
Language Acquisition ◽  
Supervised Learning ◽  
High Speed ◽  
Learning Algorithm ◽  
Syntactic Structure ◽  
Training Data ◽  
Linguistic Information ◽  
Fuzzy Language ◽  
Learning Scheme ◽  
Very High

This paper proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for example, the phrase “move forward” represents the meaningful semantic action and the phrase “very high speed” represents the linguistic information in the fuzzy command “Move forward in a very high speed.”). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on α-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.

Classification Methods

2011 ◽  
pp. 144-149 ◽  
Author(s):  
Aijun An
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
A Priori ◽  
Training Data ◽  
Classification Model ◽  
Data Set ◽  
Data Object ◽  
Unseen Data ◽  
Data Objects ◽  
Class Variable

Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, distinguished from unsupervised learning. In supervised learning, the training data consists of pairs of input data (typically vectors), and desired outputs, while in unsupervised learning there is no a priori output.

scholarly journals Semi-supervised learning: a brief review

2018 ◽  
Vol 7 (1.8) ◽  
pp. 81 ◽  
Author(s):  
Y C A Padmanabha Reddy ◽  
P Viswanath ◽  
B Eswara Reddy
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Supervised Classification ◽  
Unlabeled Data ◽  
Application Domain ◽  
Supervised Clustering ◽  
Domain Experts ◽  
Supervised And Unsupervised Learning ◽  
Learning Techniques

Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning. 

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