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COSC 285: Introduction to Data Mining

创建日期：2024-04-19 15:23:57

所属分类：计算机computer science

标签： COSC 285

Introduction to Data Mining

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COSC 285: Introduction to Data Mining

Task: Neural Network Classification

Assignment Description:
Your assignment is building a Multilayer Neural Network classifier using feedforward back-
propagation as described in the class lectures & in your textbook. As it is a supervised algorithm,
you need to use a training data set for building your model. Use the provided data set, adult.arff
for this assignment. This file contains all of the data samples that you will train and test on, using
5-fold cross-validation (if you prefer to do 10-fold cross validation it is up to you but report).
Your classifier should classify the records based on Income attribute. (for the implementation
you can use various libraries; consult the FAQ file and/or ask on the discussion board to make
sure what you can or can not use))

Pre-processing:
• Replace missing values by replacing them by mean/mode per class (as in the previous
project)
• Normalize the numerical data using Z-score normalization (you can use any libraries for
this)

Here are the architecture and requirements of your system:
• Network Topology – create a fully connected network by creating connection from each
input node to each hidden layer nodes, and from each hidden layer nodes to each output
node(s).
• Input Layer: For categorical attributes you may decide to create one node per distinct value
or to group the domains per either domain knowledge or entropy and then create a node per
each group, in your input layer. For the numeric attributes create only one input node. (For
the age attribute your choice of handling it as numeric or discretize it into groups).
• Hidden Layer: Create one hidden layer; the number of nodes in this layer is a parameter
into your program, so that you perform experimentations and observe the result of
classification by changing this parameter (see the table for parameters!).
• Output Layer: Your choice of using either one or two nodes – specify and explain your
choice in your design document.
• Learning Rate: This is also a parameter to your program (see table for parameters!)
• Epochs: The number of iterations or Epochs is also a parameter to your program (see table!).
• Arc weights and biases: Initialize to a random number in [-0.5, 0.5] range.
Runs and Results:
You may come up with a set of runs and experimentations on your own. Experimenting with
various learning rate, various number of hidden layer nodes, to evaluate their effect. Try very
different values for the parameters so that you can do some observations and analysis. Collect the
statistics to report & analyze. Here is a suggested sample of what you can report. Make sure you
provide a good analysis on the statistics you collect:
Run (fold)#: _____
# of Epochs
(report if set
fixed or after
termination
report the
number of
iterations)
Learning
Rate
# of
Hidden
Layer
Nodes
Error @ output
node1 (after
first epoch)
Error @ output
node1 (after last
epoch)
Error @ output node 2
(after first epoch)
Error @ output node 2
(after last epoch)
EVALUATE
QUALITY
(Prec. Rec., F1)
Training
Time
1/t
(t is
iteration
number)

REQUIRED: Add additional rows in this table to report results by varying the number of nodes, learning rate, and number of
epochs. Pay attention how to plan your experimentation so that you can have a meaningful analysis.
When writing your analysis also give a comparison with the results of your project-1 (Naïve
Bayes); also compare against the majority class baseline.

Deliverables:
Cover page (1 pt): should contain the following in the exact order as specified:
a. Status of this assignment: Complete or Incomplete. If incomplete state clearly what is
incomplete.
b.Time spent on this assignment. Approx. number of hours.
c.Things you wish you had been told prior to being given the assignment.

Design (10 pts): No code should be included in the design document. No specific template is
provided. You may draw diagram(s) to show the architecture and the flow of your software
components, and/or to provide the write-up of your design decisions.
Your working system, Results & Analysis (89 pts): A working system, satisfying the
requirements is expected. Results should be used to provide a good analysis of your classifier. Thus,
you are expected to provide a good analysis along with your results. Your result is based on 5(10)-
fold cross validation. This means for each of the 5(10) runs, you will need to report the results.
Include a text output file named adult.out that contains all of the runtime information for each fold,
this is mandatory. For each of the 5 runs, provide the specified information in the provided
table. You may be asked to give a demo, demonstrating that all the requirements are
implemented and functional; and answer related questions.

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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