Introduction to machine learning.

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  William R. Burrows,et al.  CART Decision-Tree Statistical Analysis and Prediction of Summer Season Maximum Surface Ozone for the Vancouver, Montreal, and Atlantic Regions of Canada , 1995 .

[3]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[4]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[5]  Ambedkar Dukkipati,et al.  Learning by Stretching Deep Networks , 2014, ICML.

[6]  Christos Schizas,et al.  Region based Support Vector Machine algorithm for medical diagnosis on Pima Indian Diabetes dataset , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Louise C. Showe,et al.  Bioinformatics Original Paper Combining Multi-species Genomic Data for Microrna Identification Using a Naı¨ve Bayes Classifier , 2022 .

[10]  Taskin Koçak,et al.  Survey of random neural network applications , 2000, Eur. J. Oper. Res..

[11]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[12]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[13]  Ian Witten,et al.  Data Mining , 2000 .

[14]  Mikhail Belkin,et al.  Semi-Supervised Learning , 2021, Machine Learning.

[15]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[16]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[17]  O. Chapelle,et al.  Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.

[18]  Christos Christodoulou,et al.  Support Vector Machines for Antenna Array Processing and Electromagnetics , 2006, Support Vector Machines for Antenna Array Processing and Electromagnetics.

[19]  A. Patle,et al.  SVM kernel functions for classification , 2013, 2013 International Conference on Advances in Technology and Engineering (ICATE).

[20]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Stelios Timotheou,et al.  The Random Neural Network: A Survey , 2010, Comput. J..

[23]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[24]  Gary Geunbae Lee,et al.  Information gain and divergence-based feature selection for machine learning-based text categorization , 2006, Inf. Process. Manag..

[25]  Christos Christopoulos,et al.  The Transmission-Line Modeling (TLM) Method in Electromagnetics , 2006, The TLM Method in Electromagnetics.

[26]  Marc M. Van Hulle Self-organizing Maps , 2012, Handbook of Natural Computing.

[27]  Mahdi Hasanlou,et al.  A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA , 2014 .

[28]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[29]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[30]  Gunnar Rätsch,et al.  The SHOGUN Machine Learning Toolbox , 2010, J. Mach. Learn. Res..

[31]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[32]  Andrew W. Senior,et al.  Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.

[33]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[34]  Xiangang Li,et al.  Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  David E. Booth,et al.  A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms , 2005, Expert Syst. Appl..

[36]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[37]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[38]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[39]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[40]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[41]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[42]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[43]  Junde Song,et al.  Signal Classification Based on Spectral Correlation Analysis and SVM in Cognitive Radio , 2008, 22nd International Conference on Advanced Information Networking and Applications (aina 2008).

[44]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[45]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[46]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[47]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[48]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[50]  Meng Chang Chen,et al.  Using chi-square statistics to measure similarities for text categorization , 2011, Expert Syst. Appl..

[51]  Lei Liu,et al.  Feature selection with dynamic mutual information , 2009, Pattern Recognit..

[52]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.