ILA-2: An Inductive Learning Algorithm over uncertain data

ABSTRACT AND CONCLUSION NEEDS TO BE RE-WRITTEN. ESPECIALLY WE SHOULD EMPHASIZE OUR CONTRIBUTION AND ORGINALITY OF THE WORK IN CONCLUSION. In this paper we describe the ILA-2 rule induction algorithm from the machine learning domain. ILA2 is the improved version of a novel inductive learning algorithm, namely ILA. We first describe the basic algorithm ILA, then present how the algorithm was improved. We also compare ILA-2 to a range of induction algorithms, including ILA. According to the empirical comparisons, ILA-2 appears to be comparable to CN2 and C4.5 algorithms in terms of output classifiers’ accuracy and size.AND CONCLUSION NEEDS TO BE RE-WRITTEN. ESPECIALLY WE SHOULD EMPHASIZE OUR CONTRIBUTION AND ORGINALITY OF THE WORK IN CONCLUSION. In this paper we describe the ILA-2 rule induction algorithm from the machine learning domain. ILA2 is the improved version of a novel inductive learning algorithm, namely ILA. We first describe the basic algorithm ILA, then present how the algorithm was improved. We also compare ILA-2 to a range of induction algorithms, including ILA. According to the empirical comparisons, ILA-2 appears to be comparable to CN2 and C4.5 algorithms in terms of output classifiers’ accuracy and size.

[1]  Jason Catlett,et al.  On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.

[2]  Arno Siebes,et al.  Data Mining: the search for knowledge in databases. , 1994 .

[3]  R. Mike Cameron-Jones,et al.  Oversearching and Layered Search in Empirical Learning , 1995, IJCAI.

[4]  Jie Cheng,et al.  Applying machine learning to semiconductor manufacturing , 1993, IEEE Expert.

[5]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[6]  Andrew K. C. Wong,et al.  Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[8]  Ronald L. Rivest,et al.  Learning decision lists , 2004, Machine Learning.

[9]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[10]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[11]  Nada Lavrac,et al.  The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.

[12]  Douglas H. Fisher,et al.  A Case Study of Incremental Concept Induction , 1986, AAAI.

[13]  Michael J. Pazzani,et al.  HYDRA: A Noise-tolerant Relational Concept Learning Algorithm , 1993, IJCAI.

[14]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[15]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[16]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[17]  Thomas E. McKee.  Predicting bankruptcy via induction , 1995, J. Inf. Technol..

[18]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[19]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[20]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[21]  Chris Carter,et al.  Assessing Credit Card Applications Using Machine Learning , 1987, IEEE Expert.

[22]  J. Zhang,et al.  A method that combines inductive learning with exemplar-based learning , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[23]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[24]  Vijay V. Raghavan,et al.  Data Mining: Research Trends, Challenges, and Applications , 1997 .

[25]  Christopher J. Thornton,et al.  Techniques in computational learning - an introduction , 1992, Chapman and Hall Computing Series.

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

[27]  Steven L. Salzberg.  On Comparing Classifiers: A Critique of Current Research and Methods , 1999 .

[28]  J. Ross Quinlan,et al.  The Minimum Description Length Principle and Categorical Theories , 1994, ICML.

[29]  Simon Kasif,et al.  A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..

[30]  Thomas G. Dietterich,et al.  Learning Boolean Concepts in the Presence of Many Irrelevant Features , 1994, Artif. Intell..

[31]  R. Mike Cameron-Jones,et al.  Efficient top-down induction of logic programs , 1994, SGAR.

[32]  Paul E. Utgoff,et al.  An Improved Algorithm for Incremental Induction of Decision Trees , 1994, ICML.

[33]  Ron Kohavi,et al.  Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology , 1995, KDD.

[34]  Vijay V. Raghavan,et al.  A comparison of feature selection algorithms in the context of rough classifiers , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[35]  Ivan Bratko,et al.  ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users , 1987, EWSL.

[36]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[37]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[38]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[39]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[40]  W. Scott Spangler,et al.  Learning Useful Rules from Inconclusive Data , 1991, Knowledge Discovery in Databases.

[41]  Duc Truong Pham,et al.  RULES: A simple rule extraction system , 1995 .

[42]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[43]  Ryszard S. Michalski,et al.  Selection of Most Representative Training Examples and Incremental Generation of VL1 Hypotheses: The Underlying Methodology and the Description of Programs ESEL and AQ11 , 1978 .